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Liu X, Du P, Dai Z, Yi R, Liu W, Wu H, Geng D, Liu J. SRTRP-Net: A multi-task learning network for segmentation and prediction of stereotactic radiosurgery treatment response in brain metastases. Comput Biol Med 2024; 175:108503. [PMID: 38688125 DOI: 10.1016/j.compbiomed.2024.108503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/30/2024] [Accepted: 04/21/2024] [Indexed: 05/02/2024]
Abstract
Before the Stereotactic Radiosurgery (SRS) treatment, it is of great clinical significance to avoid secondary genetic damage and guide the personalized treatment plans for patients with brain metastases (BM) by predicting the response to SRS treatment of brain metastatic lesions. Thus, we developed a multi-task learning model termed SRTRP-Net to provide prior knowledge of BM ROI and predict the SRS treatment response of the lesion. In dual-encoder tumor segmentation Network (DTS-Net), two parallel encoders encode the original and mirrored multi-modal MRI images. The differences in the dual-encoder features between foreground and background are enhanced by the symmetrical visual difference block (SVDB). In the bottom layer of the encoder, a transformer is used to extract local contextual features in the spatial and depth dimensions of low-resolution images. Then, the decoder of DTS-Net provides the prior knowledge for predicting the response to SRS treatment by performing BM segmentation. SRS response prediction network (SRP-Net) directly utilizes shared multi-modal MRI features weighted by the signed distance map (SDM) of the masks. The bidirectional multi-dimensional feature fusion module (BMDF) fuses the shared features and the clinical text information features to obtain comprehensive tumor information for characterizing tumors and predicting SRS treatment response. Experiments based on internal and external clinical datasets have shown that SRTRP-Net achieves comparable or better results. We believe that SRTRP-Net can help clinicians accurately develop personalized first-time treatment regimens for BM patients and improve their survival.
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Affiliation(s)
- Xiao Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100089, China.
| | - Peng Du
- Department of Radiology, the Second Affiliated Hospital of Xuzhou Medical University, Jiangsu, 221000, China.
| | - Zhiguang Dai
- CSSC Systems Engineering Research Institute, Beijing, 100094, China.
| | - Rumeng Yi
- CSSC Systems Engineering Research Institute, Beijing, 100094, China.
| | - Weifan Liu
- College of Science, Beijing Forestry University, Beijing, 100089, China.
| | - Hao Wu
- Huashan Hospital, Fudan University, Shanghai, 200020, China.
| | - Daoying Geng
- Huashan Hospital, Fudan University, Shanghai, 200020, China.
| | - Jie Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100089, China.
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2
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Mercaldo F, Brunese L, Martinelli F, Santone A, Cesarelli M. Explainable Convolutional Neural Networks for Brain Cancer Detection and Localisation. SENSORS (BASEL, SWITZERLAND) 2023; 23:7614. [PMID: 37688069 PMCID: PMC10490676 DOI: 10.3390/s23177614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/07/2023] [Accepted: 08/31/2023] [Indexed: 09/10/2023]
Abstract
Brain cancer is widely recognised as one of the most aggressive types of tumors. In fact, approximately 70% of patients diagnosed with this malignant cancer do not survive. In this paper, we propose a method aimed to detect and localise brain cancer, starting from the analysis of magnetic resonance images. The proposed method exploits deep learning, in particular convolutional neural networks and class activation mapping, in order to provide explainability by highlighting the areas of the medical image related to brain cancer (from the model point of view). We evaluate the proposed method with 3000 magnetic resonances using a free available dataset. The results we obtained are encouraging. We reach an accuracy ranging from 97.83% to 99.67% in brain cancer detection by exploiting four different models: VGG16, ResNet50, Alex_Net, and MobileNet, thus showing the effectiveness of the proposed method.
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Affiliation(s)
- Francesco Mercaldo
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy; (L.B.); (A.S.)
- Institute for Informatics and Telematics, National Research Council of Italy, 56121 Pisa, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy; (L.B.); (A.S.)
| | - Fabio Martinelli
- Institute for Informatics and Telematics, National Research Council of Italy, 56121 Pisa, Italy
| | - Antonella Santone
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy; (L.B.); (A.S.)
| | - Mario Cesarelli
- Department of Engineering, University of Sannio, 82100 Benevento, Italy;
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3
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Abbaspour Onari M, Jahangoshai Rezaee M. Implementing bargaining game-based fuzzy cognitive map and mixed-motive games for group decisions in the healthcare supplier selection. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10432-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
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4
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Rahman AU, Saeed M, Saeed MH, Zebari DA, Albahar M, Abdulkareem KH, Al-Waisy AS, Mohammed MA. A Framework for Susceptibility Analysis of Brain Tumours Based on Uncertain Analytical Cum Algorithmic Modeling. Bioengineering (Basel) 2023; 10:bioengineering10020147. [PMID: 36829641 PMCID: PMC9952481 DOI: 10.3390/bioengineering10020147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/13/2023] [Accepted: 01/17/2023] [Indexed: 01/24/2023] Open
Abstract
Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients' susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain tumours are approximated with respect to the most relevant symptoms (parameters) based on the expert opinions of decision makers in terms of complex intuitionistic fuzzy numbers (CIFNs). After determining the fuzzy parameterised values of multi-argument-based tuples and converting the CIFNs into fuzzy values, the scores for such types of tumours are computed based on a core matrix which relates them with fuzzy parameterised multi-argument-based tuples. The sub-intervals within [0, 1] denote the susceptibility degrees of patients corresponding to these types of brain tumours. The susceptibility of patients is examined by observing the membership of score values in the sub-intervals.
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Affiliation(s)
- Atiqe Ur Rahman
- Department of Mathematics, University of Management and Technology, Lahore 54000, Pakistan
| | - Muhammad Saeed
- Department of Mathematics, University of Management and Technology, Lahore 54000, Pakistan
| | - Muhammad Haris Saeed
- Department of Chemistry, University of Management and Technology, Lahore 54000, Pakistan
| | - Dilovan Asaad Zebari
- Department of Computer Science, College of Science, Nawroz University, Duhok 42001, Iraq
| | - Marwan Albahar
- School of Computer Science, Umm Al Qura University, Mecca 24211, Saudi Arabia
- Correspondence: (M.A.); (M.A.M.)
| | | | - Alaa S. Al-Waisy
- Computer Technologies Engineering Department, Information Technology College, Imam Ja’afar Al-Sadiq University, Baghdad 10001, Iraq
| | - Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq
- Correspondence: (M.A.); (M.A.M.)
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5
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Balaha HM, Hassan AES. A variate brain tumor segmentation, optimization, and recognition framework. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10337-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Dong X, Ali Z, Mahmood T, Liu P. Yager aggregation operators based on complex interval-valued q-rung orthopair fuzzy information and their application in decision making. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00901-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractAs a more massive feasible and prominent tool than the complex interval-valued Pythagorean fuzzy (CIVPF) set and complex interval-valued intuitionistic fuzzy (CIVIF) set, the complex interval-valued q-rung orthopair fuzzy (CIVQROF) set has been usually used to represent ambiguity and vagueness for real-life decision-making problems. In this paper, we firstly proposed some distance measures, Yager operational laws, and their comparison method. Further, we developed CIVQROF Yager weighted averaging (CIVQROFYWA), CIVQROF Yager ordered weighted averaging (CIVQROFYOWA), CIVQROF Yager weighted geometric (CIVQROFYWG), CIVQROF Yager ordered weighted geometric (CIVQROFYOWG) operators with CIVQROF information, and some certain well-known and feasible properties and outstanding results are explored in detail. Moreover, we proposed a new and valuable technique for handling multi-attribute decision-making problems with CIVQROF information. Lastly, a practical evaluation regarding the high blood pressure diseases of the patient is evaluated to illustrate the feasibility and worth of the proposed approaches.
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Tandel GS, Tiwari A, Kakde O. Performance enhancement of MRI-based brain tumor classification using suitable segmentation method and deep learning-based ensemble algorithm. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.104018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Haq EU, Jianjun H, Huarong X, Li K, Weng L. A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:6446680. [PMID: 36035291 PMCID: PMC9400402 DOI: 10.1155/2022/6446680] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/13/2022] [Accepted: 04/20/2022] [Indexed: 11/17/2022]
Abstract
Conventional medical imaging and machine learning techniques are not perfect enough to correctly segment the brain tumor in MRI as the proper identification and segmentation of tumor borders are one of the most important criteria of tumor extraction. The existing approaches are time-consuming, incursive, and susceptible to human mistake. These drawbacks highlight the importance of developing a completely automated deep learning-based approach for segmentation and classification of brain tumors. The expedient and prompt segmentation and classification of a brain tumor are critical for accurate clinical diagnosis and adequately treatment. As a result, deep learning-based brain tumor segmentation and classification algorithms are extensively employed. In the deep learning-based brain tumor segmentation and classification technique, the CNN model has an excellent brain segmentation and classification effect. In this work, an integrated and hybrid approach based on deep convolutional neural network and machine learning classifiers is proposed for the accurate segmentation and classification of brain MRI tumor. A CNN is proposed in the first stage to learn the feature map from image space of brain MRI into the tumor marker region. In the second step, a faster region-based CNN is developed for the localization of tumor region followed by region proposal network (RPN). In the last step, a deep convolutional neural network and machine learning classifiers are incorporated in series in order to further refine the segmentation and classification process to obtain more accurate results and findings. The proposed model's performance is assessed based on evaluation metrics extensively used in medical image processing. The experimental results validate that the proposed deep CNN and SVM-RBF classifier achieved an accuracy of 98.3% and a dice similarity coefficient (DSC) of 97.8% on the task of classifying brain tumors as gliomas, meningioma, or pituitary using brain dataset-1, while on Figshare dataset, it achieved an accuracy of 98.0% and a DSC of 97.1% on classifying brain tumors as gliomas, meningioma, or pituitary. The segmentation and classification results demonstrate that the proposed model outperforms state-of-the-art techniques by a significant margin.
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Affiliation(s)
- Ejaz Ul Haq
- Guangdong Key Laboratory of Intelligent Information Processing, School of Electronics and Information Engineering, Shenzhen University, China
- School of Computer and Information Engineering, Xiamen University of Technology, China
| | - Huang Jianjun
- Guangdong Key Laboratory of Intelligent Information Processing, School of Electronics and Information Engineering, Shenzhen University, China
| | - Xu Huarong
- School of Computer and Information Engineering, Xiamen University of Technology, China
| | - Kang Li
- Guangdong Key Laboratory of Intelligent Information Processing, School of Electronics and Information Engineering, Shenzhen University, China
| | - Lifen Weng
- School of Computer and Information Engineering, Xiamen University of Technology, China
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9
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Yu T, Gan Q, Feng G, Han G. A new fuzzy cognitive maps classifier based on capsule network. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108950] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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10
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Rammurthy D, Mahesh P. Whale Harris hawks optimization based deep learning classifier for brain tumor detection using MRI images. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2020.08.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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11
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Zahoor MM, Qureshi SA, Bibi S, Khan SH, Khan A, Ghafoor U, Bhutta MR. A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI. SENSORS 2022; 22:s22072726. [PMID: 35408340 PMCID: PMC9002515 DOI: 10.3390/s22072726] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/24/2022] [Accepted: 03/28/2022] [Indexed: 02/05/2023]
Abstract
Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.
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Affiliation(s)
- Mirza Mumtaz Zahoor
- Department of Computer & Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 45650, Pakistan; (M.M.Z.); (S.A.Q.); (S.H.K.); (A.K.)
- Pattern Recognition Lab, (DCIS), PIEAS, Islamabad 45650, Pakistan
- Faculty of Computer Science, Ibadat International University, Islamabad 54590, Pakistan
| | - Shahzad Ahmad Qureshi
- Department of Computer & Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 45650, Pakistan; (M.M.Z.); (S.A.Q.); (S.H.K.); (A.K.)
- Pattern Recognition Lab, (DCIS), PIEAS, Islamabad 45650, Pakistan
| | - Sameena Bibi
- Department of Mathematics, Air University, Islamabad 44000, Pakistan;
| | - Saddam Hussain Khan
- Department of Computer & Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 45650, Pakistan; (M.M.Z.); (S.A.Q.); (S.H.K.); (A.K.)
- Pattern Recognition Lab, (DCIS), PIEAS, Islamabad 45650, Pakistan
- Department of Computer System Engineering, University of Engineering and Applied Science (UEAS), Swat 19060, Pakistan
| | - Asifullah Khan
- Department of Computer & Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 45650, Pakistan; (M.M.Z.); (S.A.Q.); (S.H.K.); (A.K.)
- Pattern Recognition Lab, (DCIS), PIEAS, Islamabad 45650, Pakistan
- PIEAS Artificial Intelligence Center (PAIC), PIEAS, Islamabad 45650, Pakistan
| | - Usman Ghafoor
- Department of Mechanical Engineering, Institute of Space Technology, Islamabad 44000, Pakistan
- School of Mechanical Engineering, Pusan National University, Busan 46241, Korea
- Correspondence: (U.G.); (M.R.B.)
| | - Muhammad Raheel Bhutta
- Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
- Correspondence: (U.G.); (M.R.B.)
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12
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A survey on artificial intelligence techniques for chronic diseases: open issues and challenges. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10084-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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13
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Garg H, Ali Z, Yang Z, Mahmood T, Aljahdali S. Multi-criteria decision-making algorithm based on aggregation operators under the complex interval-valued q-rung orthopair uncertain linguistic information. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-210442] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The paper aims to present a concept of a Complex interval-valued q-rung orthopair uncertain linguistic set (CIVQROULS) and investigated their properties. In the presented set, the membership grades are considered in terms of the interval numbers under the complex domain while the linguistic features are added to address the uncertainties in the data. To further discuss more, we have presented the operation laws and score function for CIVQROULS. In addition to them, we present some averaging and geometric operators to aggregate the different pairs of the CIVQROULS. Some fundamental properties of the proposed operators are stated. Afterward, an algorithm for solving the decision-making problems is addressed based on the proposed operator using the CIVQROULS features. The applicability of the algorithm is demonstrated through a case study related to brain tumors and their effectiveness is compared with the existing studies.
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Affiliation(s)
- Harish Garg
- School of Mathematics, Thapar Institute of Engineering & Technology, Deemed University, Patiala, Punjab, India
| | - Zeeshan Ali
- Department of Mathematics & Statistics, International Islamic University Islamabad, Pakistan
| | - Zaoli Yang
- College of Economics and Management, Bejing University of Technology, Beijing, China
| | - Tahir Mahmood
- Department of Mathematics & Statistics, International Islamic University Islamabad, Pakistan
| | - Sultan Aljahdali
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
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14
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Karatzinis GD, Boutalis YS. Fuzzy cognitive networks with functional weights for time series and pattern recognition applications. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107415] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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15
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16
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Apostolopoulos ID, Groumpos PP, Apostolopoulos DJ. Advanced fuzzy cognitive maps: state-space and rule-based methodology for coronary artery disease detection. Biomed Phys Eng Express 2021; 7. [PMID: 33930876 DOI: 10.1088/2057-1976/abfd83] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/30/2021] [Indexed: 11/11/2022]
Abstract
According to the World Health Organization, 50% of deaths in European Union are caused by Cardiovascular Diseases (CVD), while 80% of premature heart diseases and strokes can be prevented. In this study, a Computer-Aided Diagnostic model for a precise diagnosis of Coronary Artery Disease (CAD) is proposed. The methodology is based on State Space Advanced Fuzzy Cognitive Maps (AFCMs), an evolution of the traditional Fuzzy Cognitive Maps. Also, a rule-based mechanism is incorporated, to further increase the knowledge of the proposed system and the interpretability of the decision mechanism. The proposed method is evaluated utilizing a CAD dataset from the Department of Nuclear Medicine of the University Hospital of Patras, in Greece. Several experiments are conducted to define the optimal parameters of the proposed AFCM. Furthermore, the proposed AFCM is compared with the traditional FCM approach and the literature. The experiments highlight the effectiveness of the AFCM approach, obtaining 85.47% accuracy in CAD diagnosis, showing an improvement of +7% over the traditional approach. It is demonstrated that the AFCM approach in developing Fuzzy Cognitive Maps outperforms the conventional approach, while it constitutes a reliable method for the diagnosis of Coronary Artery Disease.
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Affiliation(s)
- Ioannis D Apostolopoulos
- University of Patras, Medical School, Department of Medical Physics, Rio, Achaia, PC 26504, Greece
| | - Peter P Groumpos
- University of Patras, Department Electrical and Computer Engineering, Rio, Achaia, PC 26504, Greece
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Kang J, Ullah Z, Gwak J. MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers. SENSORS 2021; 21:s21062222. [PMID: 33810176 PMCID: PMC8004778 DOI: 10.3390/s21062222] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/13/2021] [Accepted: 03/17/2021] [Indexed: 11/21/2022]
Abstract
Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.
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Affiliation(s)
- Jaeyong Kang
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea; (J.K.); (Z.U.)
| | - Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea; (J.K.); (Z.U.)
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea; (J.K.); (Z.U.)
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of IT Convergence (Brain Korea PLUS 21), Korea National University of Transportation, Chungju 27469, Korea
- Correspondence: ; Tel.: +82-43-841-5852
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Irmak E. Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework. IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY, TRANSACTIONS OF ELECTRICAL ENGINEERING 2021; 45. [PMCID: PMC8061452 DOI: 10.1007/s40998-021-00426-9] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy specimens today. The current method is invasive, time-consuming and prone to manual errors. These disadvantages show how essential it is to perform a fully automated method for multi-classification of brain tumors based on deep learning. This paper aims to make multi-classification of brain tumors for the early diagnosis purposes using convolutional neural network (CNN). Three different CNN models are proposed for three different classification tasks. Brain tumor detection is achieved with 99.33% accuracy using the first CNN model. The second CNN model can classify the brain tumor into five brain tumor types as normal, glioma, meningioma, pituitary and metastatic with an accuracy of 92.66%. The third CNN model can classify the brain tumors into three grades as Grade II, Grade III and Grade IV with an accuracy of 98.14%. All the important hyper-parameters of CNN models are automatically designated using the grid search optimization algorithm. To the best of author’s knowledge, this is the first study for multi-classification of brain tumor MRI images using CNN whose almost all hyper-parameters are tuned by the grid search optimizer. The proposed CNN models are compared with other popular state-of-the-art CNN models such as AlexNet, Inceptionv3, ResNet-50, VGG-16 and GoogleNet. Satisfactory classification results are obtained using large and publicly available clinical datasets. The proposed CNN models can be employed to assist physicians and radiologists in validating their initial screening for brain tumor multi-classification purposes.
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Affiliation(s)
- Emrah Irmak
- Electrical-Electronics Engineering Department, Alanya Alaaddin Keykubat University, 07425 Alanya, Antalya, Turkey
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Abstract
AbstractFuzzy cognitive maps (FCMs) have been widely applied to analyze complex, causal-based systems in terms of modeling, decision making, analysis, prediction, classification, etc. This study reviews the applications and trends of FCMs in the field of systems risk analysis to the end of August 2020. To this end, the concepts of failure, accident, incident, hazard, risk, error, and fault are focused in the context of the conventional risks of the systems. After reviewing risk-based articles, a bibliographic study of the reviewed articles was carried out. The survey indicated that the main applications of FCMs in the systems risk field were in management sciences, engineering sciences and industrial applications, and medical and biological sciences. A general trend for potential FCMs’ applications in the systems risk field is provided by discussing the results obtained from different parts of the survey study.
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Ghasemi M, Kelarestaghi M, Eshghi F, Sharifi A. AFDL: a new adaptive fuzzy dictionary learning for medical image classification. Pattern Anal Appl 2020. [DOI: 10.1007/s10044-020-00909-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Kocabey Çiftçi P, Unutmaz Durmuşoğlu ZD. A multi-stage learning-based fuzzy cognitive maps for tobacco use. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04860-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Abstract
Brain tumor classification is a challenging task in the field of medical image processing. Technology has now enabled medical doctors to have additional aid for diagnosis. We aim to classify brain tumors using MRI images, which were collected from anonymous patients and artificial brain simulators. In this article, we carry out a comparative study between Simple Artificial Neural Networks with dropout, Basic Convolutional Neural Networks (CNN), and Dilated Convolutional Neural Networks. The experimental results shed light on the high classification performance (accuracy 97%) of Dilated CNN. On the other hand, Dilated CNN suffers from the gridding phenomenon. An incremental, even number dilation rate takes advantage of the reduced computational overhead and also overcomes the adverse effects of gridding. Comparative analysis between different combinations of dilation rates for the different convolution layers, help validate the results. The computational overhead in terms of efficiency for training the model to reach an acceptable threshold accuracy of 90% is another parameter to compare the model performance.
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FDSR: A new fuzzy discriminative sparse representation method for medical image classification. Artif Intell Med 2020; 106:101876. [PMID: 32593393 DOI: 10.1016/j.artmed.2020.101876] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 05/02/2020] [Indexed: 11/22/2022]
Abstract
Recent developments in medical image analysis techniques make them essential tools in medical diagnosis. Medical imaging is always involved with different kinds of uncertainties. Managing these uncertainties has motivated extensive research on medical image classification methods, particularly for the past decade. Despite being a powerful classification tool, the sparse representation suffers from the lack of sufficient discrimination and robustness, which are required to manage the uncertainty and noisiness in medical image classification issues. It is tried to overcome this deficiency by introducing a new fuzzy discriminative robust sparse representation classifier, which benefits from the fuzzy terms in its optimization function of the dictionary learning process. In this work, we present a new medical image classification approach, fuzzy discriminative sparse representation (FDSR). The proposed fuzzy terms increase the inter-class representation difference and the intra-class representation similarity. Also, an adaptive fuzzy dictionary learning approach is used to learn dictionary atoms. FDSR is applied on Magnetic Resonance Images (MRI) from three medical image databases. The comprehensive experimental results clearly show that our approach outperforms its series of rival techniques in terms of accuracy, sensitivity, specificity, and convergence speed.
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Brunese L, Mercaldo F, Reginelli A, Santone A. An ensemble learning approach for brain cancer detection exploiting radiomic features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 185:105134. [PMID: 31675644 DOI: 10.1016/j.cmpb.2019.105134] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 09/27/2019] [Accepted: 10/15/2019] [Indexed: 05/03/2023]
Abstract
BACKGROUND AND OBJECTIVE The brain cancer is one of the most aggressive tumour: the 70% of the patients diagnosed with this malignant cancer will not survive. Early detection of brain tumours can be fundamental to increase survival rates. The brain cancers are classified into four different grades (i.e., I, II, III and IV) according to how normal or abnormal the brain cells look. The following work aims to recognize the different brain cancer grades by analysing brain magnetic resonance images. METHODS A method to identify the components of an ensemble learner is proposed. The ensemble learner is focused on the discrimination between different brain cancer grades using non invasive radiomic features. The considered radiomic features are belonging to five different groups: First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix and Gray Level Size Zone Matrix. We evaluate the features effectiveness through hypothesis testing and through decision boundaries, performance analysis and calibration plots thus we select the best candidate classifiers for the ensemble learner. RESULTS We evaluate the proposed method with 111,205 brain magnetic resonances belonging to two freely available data-sets for research purposes. The results are encouraging: we obtain an accuracy of 99% for the benign grade I and the II, III and IV malignant brain cancer detection. CONCLUSION The experimental results confirm that the ensemble learner designed with the proposed method outperforms the current state-of-the-art approaches in brain cancer grade detection starting from magnetic resonance images.
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Affiliation(s)
- Luca Brunese
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Francesco Mercaldo
- Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy; Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy.
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Antonella Santone
- Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy
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Dogu E, Albayrak YE, Tuncay E. Multidrug-resistant tuberculosis risk factors assessment with intuitionistic fuzzy cognitive maps. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-179470] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Elif Dogu
- Industrial Engineering Department, Galatasaray University, Besiktas, Istanbul, Turkey
| | - Y. Esra Albayrak
- Industrial Engineering Department, Galatasaray University, Besiktas, Istanbul, Turkey
| | - Esin Tuncay
- Yedikule Chest Diseases & Thoracic Surgery Training & Research Hospital, Zeytinburnu, Istanbul, Turkey
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Lee D, Xu H, Liu H, Miao Y. Cognitive modelling of Chinese herbal medicine's effect on breast cancer. Health Inf Sci Syst 2019; 7:20. [PMID: 31656593 DOI: 10.1007/s13755-019-0083-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 09/19/2019] [Indexed: 11/30/2022] Open
Abstract
Purpose Traditional Chinese medicine (TCM) has recently attracted increasing interests in cancer treatment. It was found that TCM-based treatment, combined with other therapies, can help improve patients' life quality. However, the existing research in TCM lacks a systematic modelling for the causal relationship of the factors related to the diagnosis and decision making. Methods In this paper, we proposed the use of fuzzy cognitive map (FCM) to represent the cognition of TCMs usage in cancer treatment. Results Through a case analysis, we analyse and summarise the effects of Chinese herbal medicine in breast cancer management. Conclusion FCMs can visually represent the cognitive knowledge, particularly the causal relationship among key factors of TCM effects and the related breast cancer status.
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Affiliation(s)
- Daniel Lee
- Harmony Chinese Medicine Osteopathy and Acupuncture, Kew, 3101 VIC Australia.,2College of Engineering and Science, Victoria University, Melbourne, Australia
| | - Hong Xu
- 2College of Engineering and Science, Victoria University, Melbourne, Australia
| | - Huai Liu
- 3Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, 3122 VIC Australia
| | - Yuan Miao
- 2College of Engineering and Science, Victoria University, Melbourne, Australia
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How to Design More Sustainable Financial Systems: The Roles of Environmental, Social, and Governance Factors in the Decision-Making Process. SUSTAINABILITY 2019. [DOI: 10.3390/su11205604] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A literature review showed that finance is a driver of sustainability. However, to achieve sustainability through finance, it is necessary to rebuild and adapt the financial system to the specifics of sustainable development. Modern financial systems can be described as one-dimensional, focusing on ensuring the economic security of transactions. Meanwhile, the growing role of risk related to non-financial factors means that the factors referred to as ESG (environmental, social, governance) become the main source threatening the stability of financial systems. Adaptation activities toward the design of so-called three-dimensional financial systems rely on incorporating ESG risk into the financial decisions of the financial institutions that make up the financial system. This is found, among other factors, in the risk assessment methodology. The general goal of the paper is to investigate which ESG criteria are incorporated into the decision-making process of financial institutions and to verify the level of sustainability of financial systems in selected OECD (Organization for Economic Cooperation and Development) countries. The main research hypothesis assumes that incorporating ESG factors into the decision-making process of financial institutions makes financial systems more sustainable. A two-stage research procedure was used to achieve the research goal. In the first stage, to determine the ESG factors that affect the level of sustainability of financial systems and identify dependencies between ESG factors incorporated by financial institutions into the decision-making process, a fuzzy cognitive map (FCM) was used. The collective map elaborating on the basis of the opinions of experts participating in the study was built using the software FCMapper_bugfix_27.1.2016. In the second stage, based on multiple-criteria decision analysis (MCDA) using the PROMETHEE method (Preference Ranking Organization Method of Enrichment Evaluation), 23 OECD countries that respect the Equator Principles were ranked according to seven groups of criteria defined for financial system assessment (financial depth, development, vulnerability, soundness, fragility, stability, and sustainability), based on a literature review. The ranking confirmed the strong position of Scandinavian countries for assuring best sustainability practices in financial institutions and in the economy. The added value of this paper can be considered at two levels: theoretical and empirical. From the theoretical point of view, it should be noted that it is the first of this kind of analysis which prioritizes ESG factors in financial decisions and ranks financial systems according to fulfilling sustainability criteria. The original empirical approach based on the two-stage research procedure provided analysis of 62 factors, of which 21 represented the environmental scope, 25 the social scope, and 16 the governance scope, which is the main advantage of the empirical study presented in the paper.
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Pillutla VS, Giabbanelli PJ. Iterative generation of insight from text collections through mutually reinforcing visualizations and fuzzy cognitive maps. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2018.12.020] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Falcon R, Nápoles G, Bello R, Vanhoof K. Granular cognitive maps: a review. GRANULAR COMPUTING 2018. [DOI: 10.1007/s41066-018-0104-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Jayashree L, Lakshmi Devi R, Papandrianos N, Papageorgiou EI. Application of Fuzzy Cognitive Map for geospatial dengue outbreak risk prediction of tropical regions of Southern India. INTELLIGENT DECISION TECHNOLOGIES 2018. [DOI: 10.3233/idt-180330] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- L.S. Jayashree
- Computer Science Engineering, PSG College of Technology, Coimbatore, India
| | - R. Lakshmi Devi
- Affiliations as Computer Applications, GSS Jain College, Chennai, India
| | - Nikolaos Papandrianos
- Nursing Department, Technological Educational Institute of Central Greece, Lamia 35100, Greece
| | - Elpiniki I. Papageorgiou
- Computer Engineering Department, Technological Educational Institute of Central Greece, Lamia 35100, Greece
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Cover GS, Herrera WG, Bento MP, Appenzeller S, Rittner L. Computational methods for corpus callosum segmentation on MRI: A systematic literature review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 154:25-35. [PMID: 29249344 DOI: 10.1016/j.cmpb.2017.10.025] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2016] [Revised: 10/23/2017] [Accepted: 10/30/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE The corpus callosum (CC) is the largest white matter structure in the brain and has a significant role in central nervous system diseases. Its volume correlates with the severity and/or extent of neurodegenerative disease. Even though the CC's role has been extensively studied over the last decades, and different algorithms and methods have been published regarding CC segmentation and parcellation, no reviews or surveys covering such developments have been reported so far. To bridge this gap, this paper presents a systematic literature review of computational methods focusing on CC segmentation and parcellation acquired on magnetic resonance imaging. METHODS IEEExplore, PubMed, EBSCO Host, and Scopus database were searched with the following search terms: ((Segmentation OR Parcellation) AND (Corpus Callosum) AND (DTI OR MRI OR Diffusion Tensor Imag* OR Diffusion Tractography OR Magnetic Resonance Imag*)), resulting in 802 publications. Two reviewers independently evaluated all articles and 36 studies were selected through the systematic literature review process. RESULTS This work reviewed four main segmentation methods groups: model-based, region-based, thresholding, and machine learning; 32 different validity metrics were reported. Even though model-based techniques are the most recurrently used for the segmentation task (13 articles), machine learning approaches achieved better outcomes of 95% when analyzing mean values for segmentation and classification metrics results. Moreover, CC segmentation is better established in T1-weighted images, having more methods implemented and also being tested in larger datasets, compared with diffusion tensor images. CONCLUSIONS The analyzed computational methods used to perform CC segmentation on magnetic resonance imaging have not yet overcome all presented challenges owing to metrics variability and lack of traceable materials.
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Affiliation(s)
- G S Cover
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas, Brazil.
| | - W G Herrera
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas, Brazil
| | - M P Bento
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas, Brazil
| | - S Appenzeller
- Rheumatology Division, Faculty of Medical Science, University of Campinas, Brazil
| | - L Rittner
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas, Brazil
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Mohan G, Subashini MM. MRI based medical image analysis: Survey on brain tumor grade classification. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.07.007] [Citation(s) in RCA: 196] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Amirkhani A, Papageorgiou EI, Mohseni A, Mosavi MR. A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 142:129-145. [PMID: 28325441 DOI: 10.1016/j.cmpb.2017.02.021] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 02/11/2017] [Accepted: 02/17/2017] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE A high percentage of medical errors, committed because of physician's lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques. Therefore, designing more efficient medical decision-support systems (MDSSs) to assist physicians in decision-making is crucially important. Through combining the properties of fuzzy logic and neural networks, fuzzy cognitive maps (FCMs) are among the latest, most efficient, and strongest artificial intelligence techniques for modeling complex systems. This review study is conducted to identify different FCM structures used in MDSS designs. The best structure for each medical application can be introduced by studying the properties of FCM structures. METHODS This paper surveys the most important decision- making methods and applications of FCMs in the medical field in recent years. To investigate the efficiency and capability of different FCM models in designing MDSSs, medical applications are categorized into four key areas: decision-making, diagnosis, prediction, and classification. Also, various diagnosis and decision support problems addressed by FCMs in recent years are reviewed with the goal of introducing different types of FCMs and determining their contribution to the improvements made in the fields of medical diagnosis and treatment. RESULTS In this survey, a general trend for future studies in this field is provided by analyzing various FCM structures used for medical purposes, and the results from each category. CONCLUSIONS Due to the unique specifications of FCMs in integrating human knowledge and experience with computer-aided techniques, they are among practical instruments for MDSS design. In the not too distant future, they will have a significant role in medical sciences.
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Affiliation(s)
- Abdollah Amirkhani
- Dept. of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
| | - Elpiniki I Papageorgiou
- Dept. of Computer Engineering, Technological Educational Institute of Central Greece, Lamia 35100, Greece.
| | - Akram Mohseni
- Dept. of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
| | - Mohammad R Mosavi
- Dept. of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
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A package-SFERCB-“Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors”. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.05.020] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Ghasemi K, Khanmohammadi M, Saligheh Rad H. Accurate grading of brain gliomas by soft independent modeling of class analogy based on non-negative matrix factorization of proton magnetic resonance spectra. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2016; 54:119-125. [PMID: 26332515 DOI: 10.1002/mrc.4326] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Revised: 07/26/2015] [Accepted: 07/29/2015] [Indexed: 06/05/2023]
Abstract
Hydrogen magnetic resonance spectroscopy ((1) H-MRS) is a non-invasive technique which provides a 'frequency-signal intensity' spectrum of biochemical compounds of tissues in the body. Although this method is currently used in human brain studies, accurate classification of in-vivo (1) H-MRS is a challenging task in the diagnosis of brain tumors. Problems such as overlapping metabolite peaks, incomplete information on background component and low signal-to-noise ratio disturb classification results of this spectroscopic method. This study presents an alternative approach to the soft independent modeling of class analogy (SIMCA) technique, using non-negative matrix factorization (NMF) for dimensionality reduction. In the adopted strategy, the performance of SIMCA was improved by application of a robust algorithm for classification in the presence of noisy measurements. Total of 219 spectra from two databases were taken by water-suppressed short echo-time (1) H-MRS, acquired from different subjects with different stages of glial brain tumors (Grade II (26 cases), grade III (24 cases), grade IV (41 cases), as well as 25 healthy cases). The SIMCA was performed using two approaches: (i) principal component analysis (PCA) and (ii) non-negative matrix factorization (NMF), as a modified approach. Square prediction error was considered to assess the class membership of the external validation set. Finally, several figures of merit such as the correct classification rate (CCR), sensitivity and specificity were calculated. Results of SIMCA based on NMF showed significant improvement in percentage of correctly classified samples, 91.4% versus 83.5% for PCA-based model in an independent test set.
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Affiliation(s)
- K Ghasemi
- Chemistry Department, Faculty of Science, Imam Khomeini International University, Qazvin, Iran
| | - M Khanmohammadi
- Chemistry Department, Faculty of Science, Imam Khomeini International University, Qazvin, Iran
| | - H Saligheh Rad
- Tehran University of Medical Sciences, Medical Physics and Biomedical Engineering Department, Keshavarz Boulevard, Tehran, Iran
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Subramanian J, Karmegam A, Papageorgiou E, Papandrianos N, Vasukie A. An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 118:280-297. [PMID: 25697987 DOI: 10.1016/j.cmpb.2015.01.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Revised: 12/09/2014] [Accepted: 01/03/2015] [Indexed: 06/04/2023]
Abstract
BACKGROUND There is a growing demand for women to be classified into different risk groups of developing breast cancer (BC). The focus of the reported work is on the development of an integrated risk prediction model using a two-level fuzzy cognitive map (FCM) model. The proposed model combines the results of the initial screening mammogram of the given woman with her demographic risk factors to predict the post-screening risk of developing BC. METHODS The level-1 FCM models the demographic risk profile. A nonlinear Hebbian learning algorithm is used to train this model and thus to help on predicting the BC risk grade based on demographic risk factors identified by domain experts. The risk grades estimated by the proposed model are validated using two standard BC risk assessment models viz. Gail and Tyrer-Cuzick. The level-2 FCM models the features of the screening mammogram concerning normal, benign and malignant cases. The data driven Hebbian learning algorithm (DDNHL) is used to train this model in order to predict the BC risk grade based on these mammographic image features. An overall risk grade is calculated by combining the outcomes of these two FCMs. RESULTS The main limitation of the Gail model of underestimating the risk level of women with strong family history is overcome by the proposed model. IBIS is a hard computing tool based on the Tyrer-Cuzick model that is comprehensive enough in covering a wide range of demographic risk factors including family history, but it generates results in terms of numeric risk score based on predefined formulae. Thus the outcome is difficult to interpret by naive users. Besides these models are based only on the demographic details and do not take into account the findings of the screening mammogram. The proposed integrated model overcomes the above described limitations of the existing models and predicts the risk level in terms of qualitative grades. The predictions of the proposed NHL-FCM model comply with the Tyrer-Cuzick model for 36 out of 40 patient cases. With respect to tumor grading, the overall classification accuracy of DDNHL-FCM using 70 real mammogram screening images is 94.3%. The testing accuracy of the proposed model using 10-fold cross validation technique outperforms other standard machine learning based inference engines. CONCLUSION In the perspective of clinical oncologists, this is a comprehensive front-end medical decision support system that assists them in efficiently assessing the expected post-screening BC risk level of the given individual and hence prescribing individualized preventive interventions and more intensive surveillance for high risk women.
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Affiliation(s)
- Jayashree Subramanian
- Computer Science Engineering, RVS College of Engineering and Technology, Coimbatore, India.
| | - Akila Karmegam
- Mechatronics Engineering, Kumaraguru College of Technology, Coimbatore, India.
| | - Elpiniki Papageorgiou
- Computer Engineering Department, Technological Educational Institute of Central Greece, 3rd KM Old National Road Lamia-Athens, 35100 Lamia, Greece.
| | | | - A Vasukie
- Electronics and Communication Engineering, Kumaraguru College of Technology, Coimbatore, India.
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Giabbanelli PJ, Crutzen R. Creating groups with similar expected behavioural response in randomized controlled trials: a fuzzy cognitive map approach. BMC Med Res Methodol 2014; 14:130. [PMID: 25495712 PMCID: PMC4292828 DOI: 10.1186/1471-2288-14-130] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2014] [Accepted: 12/08/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Controlling bias is key to successful randomized controlled trials for behaviour change. Bias can be generated at multiple points during a study, for example, when participants are allocated to different groups. Several methods of allocations exist to randomly distribute participants over the groups such that their prognostic factors (e.g., socio-demographic variables) are similar, in an effort to keep participants' outcomes comparable at baseline. Since it is challenging to create such groups when all prognostic factors are taken together, these factors are often balanced in isolation or only the ones deemed most relevant are balanced. However, the complex interactions among prognostic factors may lead to a poor estimate of behaviour, causing unbalanced groups at baseline, which may introduce accidental bias. METHODS We present a novel computational approach for allocating participants to different groups. Our approach automatically uses participants' experiences to model (the interactions among) their prognostic factors and infer how their behaviour is expected to change under a given intervention. Participants are then allocated based on their inferred behaviour rather than on selected prognostic factors. RESULTS In order to assess the potential of our approach, we collected two datasets regarding the behaviour of participants (n = 430 and n = 187). The potential of the approach on larger sample sizes was examined using synthetic data. All three datasets highlighted that our approach could lead to groups with similar expected behavioural changes. CONCLUSIONS The computational approach proposed here can complement existing statistical approaches when behaviours involve numerous complex relationships, and quantitative data is not readily available to model these relationships. The software implementing our approach and commonly used alternatives is provided at no charge to assist practitioners in the design of their own studies and to compare participants' allocations.
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Affiliation(s)
- Philippe J Giabbanelli
- />Interdisciplinary Research in the Mathematical and Computational Sciences (IRMACS) Centre, Simon Fraser University, Burnaby, Canada
- />UKCRC Centre for Diet and Activity Research, MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ UK
| | - Rik Crutzen
- />Department of Health Promotion, Maastricht University/CAPHRI, Maastricht, The Netherlands
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Anninou AP, Groumpos PP. Modeling of Parkinson's Disease Using Fuzzy Cognitive Maps and Non-Linear Hebbian Learning. INT J ARTIF INTELL T 2014. [DOI: 10.1142/s0218213014500109] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Parkinson's disease is a chronic, progressive, age-related, neurodegenerative disorder that affects a large population around the world. A mathematical model for Parkinson's disease is presented using Fuzzy Cognitive Maps (FCMs). Basic theories of FCMs are reviewed and presented. Decision Support Systems (DSS) for medical problems are reviewed. Non-linear Hebbian learning techniques are considered in studying Medical problems and a generic algorithm is presented. The proposed method used the knowledge of a number of experts and simulations were performed obtaining interesting results. Comparisons of the results of the proposed method, both by making use and not making use of learning algorithms, are presented. Some interesting future research directions are mentioned.
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Affiliation(s)
- Antigoni P. Anninou
- Laboratory for Automation and Robotics, Department of Electrical and Computer Engineering, University of Patras, 26500 Rio, Greece
| | - Peter P. Groumpos
- Laboratory for Automation and Robotics, Department of Electrical and Computer Engineering, University of Patras, 26500 Rio, Greece
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Billis AS, Papageorgiou EI, Frantzidis CA, Tsatali MS, Tsolaki AC, Bamidis PD. A decision-support framework for promoting independent living and ageing well. IEEE J Biomed Health Inform 2014; 19:199-209. [PMID: 25073180 DOI: 10.1109/jbhi.2014.2336757] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Artificial intelligence and decision support systems offer a plethora of health monitoring capabilities in ambient assisted living environment. Continuous assessment of health indicators for elderly people living on their own is of utmost importance, so as to prolong their independence and quality of life. Slow varying, long-term deteriorating health trends are not easily identifiable in seniors. Thus, early sign detection of a specific condition, as well as, any likely transition from a healthy state to a pathological one are key problems that the herein proposed framework aims at resolving. Statistical process control concepts offer a personalized approach toward identification of trends that are away from the atypical behavior or state of the seniors, while fuzzy cognitive maps knowledge representation and inference schema have proved to be efficient in terms of disease classification. Geriatric depression is used as a case study throughout the paper, so to prove the validity of the framework, which is planned to be pilot tested with a series of lone-living seniors in their own homes.
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Affiliation(s)
- Antonis S Billis
- Lab of Medical Physics, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Elpiniki I Papageorgiou
- Technological Educational Institute of Central Greece, Computer Engineering Department, Lamia, TK, Greece
| | - Christos A Frantzidis
- Lab of Medical Physics, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Marianna S Tsatali
- Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Greece
| | - Anthoula C Tsolaki
- Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Greece
| | - Panagiotis D Bamidis
- Lab of Medical Physics, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Classification of Intraductal Breast Lesions Based on the Fuzzy Cognitive Map. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014. [DOI: 10.1007/s13369-014-1012-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Papageorgiou EI, Huszka C, De Roo J, Douali N, Jaulent MC, Colaert D. Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision support. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 112:580-598. [PMID: 23953959 DOI: 10.1016/j.cmpb.2013.07.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2012] [Revised: 07/15/2013] [Accepted: 07/17/2013] [Indexed: 06/02/2023]
Abstract
This study aimed to focus on medical knowledge representation and reasoning using the probabilistic and fuzzy influence processes, implemented in the semantic web, for decision support tasks. Bayesian belief networks (BBNs) and fuzzy cognitive maps (FCMs), as dynamic influence graphs, were applied to handle the task of medical knowledge formalization for decision support. In order to perform reasoning on these knowledge models, a general purpose reasoning engine, EYE, with the necessary plug-ins was developed in the semantic web. The two formal approaches constitute the proposed decision support system (DSS) aiming to recognize the appropriate guidelines of a medical problem, and to propose easily understandable course of actions to guide the practitioners. The urinary tract infection (UTI) problem was selected as the proof-of-concept example to examine the proposed formalization techniques implemented in the semantic web. The medical guidelines for UTI treatment were formalized into BBN and FCM knowledge models. To assess the formal models' performance, 55 patient cases were extracted from a database and analyzed. The results showed that the suggested approaches formalized medical knowledge efficiently in the semantic web, and gave a front-end decision on antibiotics' suggestion for UTI.
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Affiliation(s)
- Elpiniki I Papageorgiou
- Department of Computer Engineering, Technological Educational Institute of Central Greece, 3rd Km Old National Road Lamia-Athens, 35100 Lamia, Greece.
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Huml M, Silye R, Zauner G, Hutterer S, Schilcher K. Brain tumor classification using AFM in combination with data mining techniques. BIOMED RESEARCH INTERNATIONAL 2013; 2013:176519. [PMID: 24062997 PMCID: PMC3766995 DOI: 10.1155/2013/176519] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2013] [Accepted: 07/18/2013] [Indexed: 12/21/2022]
Abstract
Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.
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Affiliation(s)
- Marlene Huml
- School of Applied Health and Social Sciences, University of Applied Sciences Upper Austria, Garnisonstraße 21, 4020 Linz, Austria
| | - René Silye
- Department of Pathology, Nerve Clinic Linz Wagner Jauregg, Wagner-Jauregg-Weg 15, 4020 Linz, Austria
| | - Gerald Zauner
- University of Applied Sciences Upper Austria, Research & Development Wels, Stelzhamerstraße 23, 4600 Wels, Austria
| | - Stephan Hutterer
- University of Applied Sciences Upper Austria, Research & Development Wels, Stelzhamerstraße 23, 4600 Wels, Austria
| | - Kurt Schilcher
- School of Applied Health and Social Sciences, University of Applied Sciences Upper Austria, Garnisonstraße 21, 4020 Linz, Austria
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State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 2013; 31:1426-38. [PMID: 23790354 DOI: 10.1016/j.mri.2013.05.002] [Citation(s) in RCA: 221] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2012] [Revised: 05/04/2013] [Accepted: 05/05/2013] [Indexed: 11/22/2022]
Abstract
Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized.
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Berrang-Ford L, Garton K. Expert knowledge sourcing for public health surveillance: national tsetse mapping in Uganda. Soc Sci Med 2013; 91:246-55. [PMID: 23608601 DOI: 10.1016/j.socscimed.2013.03.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2012] [Revised: 03/04/2013] [Accepted: 03/04/2013] [Indexed: 11/16/2022]
Abstract
In much of sub-Saharan Africa, availability of standardized and reliable public health data is poor or negligible. Despite continued calls for the prioritization of improved health datasets in poor regions, public health surveillance remains a significant global health challenge. Alternate approaches to surveillance and collection of public health data have thus garnered increasing interest, though there remains relatively limited research evaluating these approaches for public health. Herein, we present a case study applying and evaluating the use of expert knowledge sources for public health dataset development, using the case of vector distributions of Human African Trypanosomiasis (HAT) in Uganda. Specific objectives include: 1) Review the use of expert knowledge sourcing methods for public health surveillance, 2) Review current knowledge on tsetse vector distributions of public health importance in Uganda and the methods used for tsetse mapping in Africa; 3) Quantify confidence of the presence or absence of tsetse flies in Uganda based on expert informant reports, and 4) Assess the reliability and potential utility of expert knowledge sourcing as an alternative or complimentary method for public health surveillance in general and tsetse mapping in particular. Information on tsetse presence or absence, and associated confidence, was collected through interviews with District Entomologist and Veterinary Officers to develop a database of tsetse distributions for 952 sub-counties in Uganda. Results show high consistency with existing maps, indicating potential reliability of modeling approaches, though failing to provide evidence for successful tsetse control in past decades. Expert-sourcing methods provide a novel, low-cost and rapid complimentary approach for triangulating data from prediction modeling where field-based validation is not feasible. Data quality is dependent, however, on the level of expertise and documentation to support confidence levels for data reporting. Results highlight the need for increased evaluation of alternate approaches and methods to data collection.
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Affiliation(s)
- Lea Berrang-Ford
- Department of Geography, McGill University, 805 Sherbrooke Street Ouest, Montreal, Quebec, H3A0B9, Canada.
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León M, Nápoles G, Bello R, Mkrtchyan L, Depaire B, Vanhoof K. Tackling Travel Behaviour: An approach based on Fuzzy Cognitive Maps. INT J COMPUT INT SYS 2013. [DOI: 10.1080/18756891.2013.816025] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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Kottas T, Boutalis Y, Christodoulou M. Bi-linear adaptive estimation of Fuzzy Cognitive Networks. Appl Soft Comput 2012. [DOI: 10.1016/j.asoc.2012.01.025] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Giabbanelli PJ, Torsney-Weir T, Mago VK. A fuzzy cognitive map of the psychosocial determinants of obesity. Appl Soft Comput 2012. [DOI: 10.1016/j.asoc.2012.02.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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