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Munuswamy Selvaraj K, Gnanagurusubbiah S, Roby Roy RR, John Peter JH, Balu S. Enhancing skin lesion classification with advanced deep learning ensemble models: a path towards accurate medical diagnostics. Curr Probl Cancer 2024; 49:101077. [PMID: 38480028 DOI: 10.1016/j.currproblcancer.2024.101077] [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: 08/21/2023] [Revised: 01/27/2024] [Accepted: 02/28/2024] [Indexed: 04/29/2024]
Abstract
Skin cancer, including the highly lethal malignant melanoma, poses a significant global health challenge with a rising incidence rate. Early detection plays a pivotal role in improving survival rates. This study aims to develop an advanced deep learning-based approach for accurate skin lesion classification, addressing challenges such as limited data availability, class imbalance, and noise. Modern deep neural network designs, such as ResNeXt101, SeResNeXt101, ResNet152V2, DenseNet201, GoogLeNet, and Xception, which are used in the study and ze optimised using the SGD technique. The dataset comprises diverse skin lesion images from the HAM10000 and ISIC datasets. Noise and artifacts are tackled using image inpainting, and data augmentation techniques enhance training sample diversity. The ensemble technique is utilized, creating both average and weighted average ensemble models. Grid search optimizes model weight distribution. The individual models exhibit varying performance, with metrics including recall, precision, F1 score, and MCC. The "Average ensemble model" achieves harmonious balance, emphasizing precision, F1 score, and recall, yielding high performance. The "Weighted ensemble model" capitalizes on individual models' strengths, showcasing heightened precision and MCC, yielding outstanding performance. The ensemble models consistently outperform individual models, with the average ensemble model attaining a macro-average ROC-AUC score of 96 % and the weighted ensemble model achieving a macro-average ROC-AUC score of 97 %. This research demonstrates the efficacy of ensemble techniques in significantly improving skin lesion classification accuracy. By harnessing the strengths of individual models and addressing their limitations, the ensemble models exhibit robust and reliable performance across various metrics. The findings underscore the potential of ensemble techniques in enhancing medical diagnostics and contributing to improved patient outcomes in skin lesion diagnosis.
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Affiliation(s)
- Kavitha Munuswamy Selvaraj
- Department of Electronics and Communication Engineering, R.M.K. Engineering College, RSM Nagar, Chennai, Tamil Nadu, India.
| | - Sumathy Gnanagurusubbiah
- Department of Computational Intelligence, SRM Institute of Science and Technology, kattankulathur, Tamil Nadu, India
| | - Reena Roy Roby Roy
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
| | - Jasmine Hephzipah John Peter
- Department of Electronics and Communication Engineering, R.M.K. Engineering College, RSM Nagar, Chennai, Tamil Nadu, India
| | - Sarala Balu
- Department of Electronics and Communication Engineering, R.M.K. Engineering College, RSM Nagar, Chennai, Tamil Nadu, India
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Pan CT, Kumar R, Wen ZH, Wang CH, Chang CY, Shiue YL. Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging. Diagnostics (Basel) 2024; 14:500. [PMID: 38472972 DOI: 10.3390/diagnostics14050500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 02/16/2024] [Accepted: 02/18/2024] [Indexed: 03/14/2024] Open
Abstract
The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support early treatment interventions. This study introduces an innovative two-stage data analytics framework that leverages deep learning algorithms through a strategic combinatorial fusion technique, aimed at refining the accuracy of early-stage diagnosis of such infections. Utilizing a comprehensive dataset compiled from publicly available lung X-ray images, the research employs advanced pre-trained deep learning models to navigate the complexities of disease classification, addressing inherent data imbalances through methodical validation processes. The core contribution of this work lies in its novel application of combinatorial fusion, integrating select models to significantly elevate diagnostic precision. This approach not only showcases the adaptability and strength of deep learning in navigating the intricacies of medical imaging but also marks a significant step forward in the utilization of artificial intelligence to improve outcomes in healthcare diagnostics. The study's findings illuminate the path toward leveraging technological advancements in enhancing diagnostic accuracies, ultimately contributing to the timely and effective treatment of respiratory diseases.
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Affiliation(s)
- Cheng-Tang Pan
- Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan
- Institute of Precision Medicine, National Sun Yat-sen University, Kaohsiung 804, Taiwan
- Taiwan Instrument Research Institute, National Applied Research Laboratories, Hsinchu 300, Taiwan
- Institute of Advanced Semiconductor Packaging and Testing, College of Semiconductor and Advanced Technology Research, National Sun Yat-sen University, Kaohsiung 804, Taiwan
| | - Rahul Kumar
- Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan
| | - Zhi-Hong Wen
- Department of Marine Biotechnology and Research, National Sun Yat-sen University, Kaohsiung 804, Taiwan
| | - Chih-Hsuan Wang
- Division of Nephrology and Metabolism, Department of Internal Medicine, Kaohsiung Armed Forces General Hospital, Kaohsiung 804, Taiwan
- Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
| | - Chun-Yung Chang
- Division of Nephrology and Metabolism, Department of Internal Medicine, Kaohsiung Armed Forces General Hospital, Kaohsiung 804, Taiwan
- Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
| | - Yow-Ling Shiue
- Institute of Precision Medicine, National Sun Yat-sen University, Kaohsiung 804, Taiwan
- Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
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Zhang Y, Feng W, Wu Z, Li W, Tao L, Liu X, Zhang F, Gao Y, Huang J, Guo X. Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1088. [PMID: 37374292 DOI: 10.3390/medicina59061088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/29/2023]
Abstract
Background and Objectives: Lung cancer remains a leading cause of cancer mortality worldwide. Accurately classifying benign pulmonary nodules and malignant ones is crucial for early diagnosis and improved patient outcomes. The purpose of this study is to explore the deep-learning model of ResNet combined with a convolutional block attention module (CBAM) for the differentiation between benign and malignant lung cancer, based on computed tomography (CT) images, morphological features, and clinical information. Methods and materials: In this study, 8241 CT slices containing pulmonary nodules were retrospectively included. A random sample comprising 20% (n = 1647) of the images was used as the test set, and the remaining data were used as the training set. ResNet combined CBAM (ResNet-CBAM) was used to establish classifiers on the basis of images, morphological features, and clinical information. Nonsubsampled dual-tree complex contourlet transform (NSDTCT) combined with SVM classifier (NSDTCT-SVM) was used as a comparative model. Results: The AUC and the accuracy of the CBAM-ResNet model were 0.940 and 0.867, respectively, in test set when there were only images as inputs. By combining the morphological features and clinical information, CBAM-ResNet shows better performance (AUC: 0.957, accuracy: 0.898). In comparison, a radiomic analysis using NSDTCT-SVM achieved AUC and accuracy values of 0.807 and 0.779, respectively. Conclusions: Our findings demonstrate that deep-learning models, combined with additional information, can enhance the classification performance of pulmonary nodules. This model can assist clinicians in accurately diagnosing pulmonary nodules in clinical practice.
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Affiliation(s)
- Yanfei Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Wei Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Zhiyuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Weiming Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Lixin Tao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Xiangtong Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Feng Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Yan Gao
- Department of Nuclear Medicine, Xuanwu Hospital Capital Medical University, Beijing 100053, China
| | - Jian Huang
- School of Mathematical Sciences, University College Cork, T12 YN60 Cork, Ireland
| | - Xiuhua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
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Chen J, Wang J, Yuan Q, Yang Z. CNN-LSTM Model for Recognizing Video-Recorded Actions Performed in a Traditional Chinese Exercise. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:351-359. [PMID: 37435544 PMCID: PMC10332470 DOI: 10.1109/jtehm.2023.3282245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 02/03/2023] [Accepted: 05/30/2023] [Indexed: 07/13/2023]
Abstract
Identifying human actions from video data is an important problem in the fields of intelligent rehabilitation assessment. Motion feature extraction and pattern recognition are the two key procedures to achieve such goals. Traditional action recognition models are usually based on the geometric features manually extracted from video frames, which are however difficult to adapt to complex scenarios and cannot achieve high-precision recognition and robustness. We investigate a motion recognition model and apply it to recognize the sequence of complicated actions of a traditional Chinese exercise (ie, Baduanjin). We first developed a combined convolutional neural network (CNN) and long short-term memory (LSTM) model for recognizing the sequence of actions captured in video frames, and applied it to recognize the actions of Baduanjin. Moreover, this method has been compared with the traditional action recognition model based on geometric motion features in which Openpose is used to identify the joint positions in the skeletons. Its performance of high recognition accuracy has been verified on the testing video dataset, containing the video clips from 18 different practicers. The CNN-LSTM recognition model achieved 96.43% accuracy on the testing set; while those manually extracted features in the traditional action recognition model were only able to achieve 66.07% classification accuracy on the testing video dataset. The abstract image features extracted by the CNN module are more effective on improving the classification accuracy of the LSTM model. The proposed CNN-LSTM based method can be a useful tool in recognizing the complicated actions.
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Affiliation(s)
- Jing Chen
- School of Electronic and Information EngineeringSuzhou University of Science and TechnologySuzhou215009China
| | - Jiping Wang
- Suzhou Institute of Biomedical Engineering and TechnologySuzhou215000China
| | - Qun Yuan
- Department of Respiratory MedicineSuzhou Hospital, Affiliated Hospital of Medical School, Nanjing UniversitySuzhou215163China
| | - Zhao Yang
- Department of Respiratory MedicineSuzhou Hospital, Affiliated Hospital of Medical School, Nanjing UniversitySuzhou215163China
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5
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Le PT, Pham BT, Chang CC, Hsu YC, Tai TC, Li YH, Wang JC. Anti-Aliasing Attention U-net Model for Skin Lesion Segmentation. Diagnostics (Basel) 2023; 13:diagnostics13081460. [PMID: 37189563 DOI: 10.3390/diagnostics13081460] [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: 03/04/2023] [Revised: 04/03/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
The need for a lightweight and reliable segmentation algorithm is critical in various biomedical image-prediction applications. However, the limited quantity of data presents a significant challenge for image segmentation. Additionally, low image quality negatively impacts the efficiency of segmentation, and previous deep learning models for image segmentation require large parameters with hundreds of millions of computations, resulting in high costs and processing times. In this study, we introduce a new lightweight segmentation model, the mobile anti-aliasing attention u-net model (MAAU), which features both encoder and decoder paths. The encoder incorporates an anti-aliasing layer and convolutional blocks to reduce the spatial resolution of input images while avoiding shift equivariance. The decoder uses an attention block and decoder module to capture prominent features in each channel. To address data-related problems, we implemented data augmentation methods such as flip, rotation, shear, translate, and color distortions, which enhanced segmentation efficiency in the international Skin Image Collaboration (ISIC) 2018 and PH2 datasets. Our experimental results demonstrated that our approach had fewer parameters, only 4.2 million, while it outperformed various state-of-the-art segmentation methods.
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Affiliation(s)
- Phuong Thi Le
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320, Taiwan
| | - Bach-Tung Pham
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan
| | - Ching-Chun Chang
- Department of Computer Science, University of Warwick, Coventry CV47AL, UK
| | - Yi-Chiung Hsu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320, Taiwan
| | - Tzu-Chiang Tai
- Department of Computer Science and Information Engineering, Providence University, Taichung 43301, Taiwan
| | - Yung-Hui Li
- AI Research Center, Hon Hai Research Institute, New Taipei City 236, Taiwan
| | - Jia-Ching Wang
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan
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Tomihama RT, Camara JR, Kiang SC. Machine learning analysis of confounding variables of a convolutional neural network specific for abdominal aortic aneurysms. JVS Vasc Sci 2023. [DOI: 10.1016/j.jvssci.2022.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
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7
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Chao J, Zhang Y. Analysis of the Current Situation of Teaching and Learning of Ideological and Political Theory Courses by Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5396054. [PMID: 36035828 PMCID: PMC9410937 DOI: 10.1155/2022/5396054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/15/2022] [Accepted: 06/22/2022] [Indexed: 11/17/2022]
Abstract
The objectives are to solve the problems existing in the current ideological and political theory courses, such as the difficulty of classroom teaching quality assessment, the confusion of teachers' classroom process management, and the lack of objective assessment basis in teaching quality monitoring. Based on Artificial Intelligence (AI) technology, a designed evaluation method is proposed for teachers' classroom teaching and solves some problems such as high system cost, low evaluation accuracy, and imperfect evaluation methods. Firstly, the boundary algorithm system is introduced in the research, and the Field Programmable Gate Array (FPGA) by deep learning (DL) is used to accelerate the server hardware network platform and equipped with pan tilt zoom (PTZ) and manage multiple AI + embedded visual boundary algorithm devices. Secondly, the network platform can manage the PTZ and focal length of Internet protocol (IP) cameras, measure, and capture face images, transmit data, and recognize students' face, head, and body postures. Finally, classroom teaching is evaluated, and students' behavioral data and functions are designed, debugged, and tested. The research results demonstrate that the method overcomes the problem of high system cost through edge computing and hardware structure, and DL technology is used to overcome the problem of low accuracy of classroom teaching evaluation. Various indicators such as attendance rate, concentration, activity, and richness of teaching links in classroom teaching are obtained. The method involved can make an objective evaluation of classroom teaching and overcome the problem of incomplete classroom teaching evaluation.
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Affiliation(s)
- Jin Chao
- Marxist Branch, Shaoxing University Yuanpei College, Shaoxing 312000, Zhejiang, China
| | - Yijiang Zhang
- Information and Mechanical and Electrical Engineering Branch, Shaoxing University Yuanpei College, Shaoxing 312000, Zhejiang, China
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Naseer I, Akram S, Masood T, Jaffar A, Khan MA, Mosavi A. Performance Analysis of State-of-the-Art CNN Architectures for LUNA16. SENSORS 2022; 22:s22124426. [PMID: 35746208 PMCID: PMC9227226 DOI: 10.3390/s22124426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 02/01/2023]
Abstract
The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional neural network architecture has gained substantial attention as well as popularity. This study focuses on the intrinsic various CNN architectures: LeNet, AlexNet, VGG16, ResNet-50, and Inception-V1, which have been scrutinized and compared with each other for the detection of lung cancer using publicly available LUNA16 datasets. Furthermore, multiple performance optimizers: root mean square propagation (RMSProp), adaptive moment estimation (Adam), and stochastic gradient descent (SGD), were applied for this comparative study. The performances of the three CNN architectures were measured for accuracy, specificity, sensitivity, positive predictive value, false omission rate, negative predictive value, and F1 score. The experimental results showed that the CNN AlexNet architecture with the SGD optimizer achieved the highest validation accuracy for CT lung cancer with an accuracy of 97.42%, misclassification rate of 2.58%, 97.58% sensitivity, 97.25% specificity, 97.58% positive predictive value, 97.25% negative predictive value, false omission rate of 2.75%, and F1 score of 97.58%. AlexNet with the SGD optimizer was the best and outperformed compared to the other state-of-the-art CNN architectures.
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Affiliation(s)
- Iftikhar Naseer
- Faculty of Computer Science & Information Technology, The Superior University, Lahore 54600, Pakistan; (I.N.); (S.A.); (T.M.); (A.J.)
| | - Sheeraz Akram
- Faculty of Computer Science & Information Technology, The Superior University, Lahore 54600, Pakistan; (I.N.); (S.A.); (T.M.); (A.J.)
| | - Tehreem Masood
- Faculty of Computer Science & Information Technology, The Superior University, Lahore 54600, Pakistan; (I.N.); (S.A.); (T.M.); (A.J.)
| | - Arfan Jaffar
- Faculty of Computer Science & Information Technology, The Superior University, Lahore 54600, Pakistan; (I.N.); (S.A.); (T.M.); (A.J.)
| | - Muhammad Adnan Khan
- Department of Software, Gachon University, Seongnam 13120, Korea
- Correspondence:
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary;
- Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, 81107 Bratislava, Slovakia
- Faculty of Civil Engineering, Technical University of Dresden, 01062 Dresden, Germany
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Huang A, Jiang L, Zhang J, Wang Q. Attention-VGG16-UNet: a novel deep learning approach for automatic segmentation of the median nerve in ultrasound images. Quant Imaging Med Surg 2022; 12:3138-3150. [PMID: 35655843 PMCID: PMC9131343 DOI: 10.21037/qims-21-1074] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 03/07/2022] [Indexed: 10/15/2023]
Abstract
BACKGROUND Ultrasonography-an imaging technique that can show the anatomical section of nerves and surrounding tissues-is one of the most effective imaging methods to diagnose nerve diseases. However, segmenting the median nerve in two-dimensional (2D) ultrasound images is challenging due to the tiny and inconspicuous size of the nerve, the low contrast of images, and imaging noise. This study aimed to apply deep learning approaches to improve the accuracy of automatic segmentation of the median nerve in ultrasound images. METHODS In this study, we proposed an improved network called VGG16-UNet, which incorporates a contracting path and an expanding path. The contracting path is the VGG16 model with the 3 fully connected layers removed. The architecture of the expanding path resembles the upsampling path of U-Net. Moreover, attention mechanisms or/and residual modules were added to the U-Net and VGG16-UNet, which sequentially obtained Attention-UNet (A-UNet), Summation-UNet (S-UNet), Attention-Summation-UNet (AS-UNet), Attention-VGG16-UNet (A-VGG16-UNet), Summation-VGG16-UNet (S-VGG16-UNet), and Attention-Summation-VGG16-UNet (AS-VGG16-UNet). Each model was trained on the dataset of 910 median nerve images from 19 participants and tested on 207 frames from a new image sequence. The performance of the models was evaluated by metrics including Dice similarity coefficient (Dice), Jaccard similarity coefficient (Jaccard), Precision, and Recall. Based on the best segmentation results, we reconstructed a 3D median nerve image using the volume rendering method in the Visualization Toolkit (VTK) to assist in clinical nerve diagnosis. RESULTS The results of paired t-tests showed significant differences (P<0.01) in the metrics' values of different models. It showed that AS-UNet ranked first in U-Net models. The VGG16-UNet and its variants performed better than the corresponding U-Net models. Furthermore, the model's performance with the attention mechanism was superior to that with the residual module either based on U-Net or VGG16-UNet. The A-VGG16-UNet achieved the best performance (Dice =0.904±0.035, Jaccard =0.826±0.057, Precision =0.905±0.061, and Recall =0.909±0.061). Finally, we applied the trained A-VGG16-UNet to segment the median nerve in the image sequence, then reconstructed and visualized the 3D image of the median nerve. CONCLUSIONS This study demonstrates that the attention mechanism and residual module improve deep learning models for segmenting ultrasound images. The proposed VGG16-UNet-based models performed better than U-Net-based models. With segmentation, a 3D median nerve image can be reconstructed and can provide a visual reference for nerve diagnosis.
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Affiliation(s)
- Aiyue Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Li Jiang
- Department of Rehabilitation, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiangshan Zhang
- Department of Rehabilitation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qing Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
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Development of a convolutional neural network to detect abdominal aortic aneurysms. J Vasc Surg Cases Innov Tech 2022; 8:305-311. [PMID: 35692515 PMCID: PMC9178344 DOI: 10.1016/j.jvscit.2022.04.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 04/02/2022] [Indexed: 11/21/2022] Open
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11
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Segmentation and classification of breast cancer using novel deep learning architecture. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07230-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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12
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Qin C, Hu W, Wang X, Ma X. Application of Artificial Intelligence in Diagnosis of Craniopharyngioma. Front Neurol 2022; 12:752119. [PMID: 35069406 PMCID: PMC8770750 DOI: 10.3389/fneur.2021.752119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/12/2021] [Indexed: 12/24/2022] Open
Abstract
Craniopharyngioma is a congenital brain tumor with clinical characteristics of hypothalamic-pituitary dysfunction, increased intracranial pressure, and visual field disorder, among other injuries. Its clinical diagnosis mainly depends on radiological examinations (such as Computed Tomography, Magnetic Resonance Imaging). However, assessing numerous radiological images manually is a challenging task, and the experience of doctors has a great influence on the diagnosis result. The development of artificial intelligence has brought about a great transformation in the clinical diagnosis of craniopharyngioma. This study reviewed the application of artificial intelligence technology in the clinical diagnosis of craniopharyngioma from the aspects of differential classification, prediction of tissue invasion and gene mutation, prognosis prediction, and so on. Based on the reviews, the technical route of intelligent diagnosis based on the traditional machine learning model and deep learning model were further proposed. Additionally, in terms of the limitations and possibilities of the development of artificial intelligence in craniopharyngioma diagnosis, this study discussed the attentions required in future research, including few-shot learning, imbalanced data set, semi-supervised models, and multi-omics fusion.
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Affiliation(s)
- Caijie Qin
- Institute of Information Engineering, Sanming University, Sanming, China
| | - Wenxing Hu
- University of New South Wales, Sydney, NSW, Australia
| | - Xinsheng Wang
- School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai, China
| | - Xibo Ma
- CBSR & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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13
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Rajappan RJ, Kondampatti Kandaswamy T. A composite framework of deep multiple view human joints feature extraction and selection strategy with hybrid adaptive sunflower optimization‐whale optimization algorithm for human action recognition in video sequences. Comput Intell 2022. [DOI: 10.1111/coin.12499] [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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14
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Kumar M, Shakya D, Kurup V, Suksatan W. COVID-19 prediction through X-ray images using transfer learning-based hybrid deep learning approach. MATERIALS TODAY: PROCEEDINGS 2022; 51:2520-2524. [PMID: 34926174 PMCID: PMC8666290 DOI: 10.1016/j.matpr.2021.12.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Over the past few months, the campaign against COVID-19 has developed into one of the world's most sought anti-toxin treatment scheme. It is fundamental to distinguish cases of COVID-19 precisely and quickly to help avoid this pandemic from taking a wrong turn with a proper medical reasoning and solution. While Reverse-Transcription Polymerase Chain Reaction (RT-PCR) has been useful in detection of corona virus, chest X-Ray techniques has proven to be more successful and beneficial at detection of the effects of virus. With the increase in COVID patients and the X-Rays done, it is currently possible to classify the X-Ray reports with transfer learning. This paper presents a novel approach, i.e., Hybrid Convolutional Neural Network (HDCNN), which integrates Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architecture for the finding of COVID-19 using the chest X-Ray. The transfer learning approach, namely slope weighted activation class planning (Grad-CAMs), is used with HDCNN to display images responsible for taking decisions. In this study, HDCNN is compared with other CNNs such as Inception-v3, ShuffleNet, SqueezeNet, VGG-19 and DenseNet. As a result, HDCNN has achieved an accuracy of 98.20%, precision of 97.31%, recall of 97.1% and F1 score of 0.97. Compared to other current deep learning models, the HDCNN has achieved better results, and this can be used for diagnosis purpose after proper approvals.
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Hellmann F, Ren Z, Andre E, Schuller BW. Deformable Dilated Faster R-CNN for Universal Lesion Detection in CT Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2896-2902. [PMID: 34891852 DOI: 10.1109/embc46164.2021.9631021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cancer is a major public health issue and takes the second-highest toll of deaths caused by non-communicable diseases worldwide. Automatically detecting lesions at an early stage is essential to increase the chance of a cure. This study proposes a novel dilated Faster R-CNN with modulated deformable convolution and modulated deformable positive-sensitive region of interest pooling to detect lesions in computer tomography images. A pre-trained VGG-16 is transferred as the backbone of Faster R-CNN, followed by a region proposal network and a region of interest pooling layer to achieve lesion detection. The modulated deformable convolutional layers are employed to learn deformable convolutional filters, while the modulated deformable positive-sensitive region of interest pooling provides an enhanced feature extraction on the feature maps. Moreover, dilated convolutions are combined with the modulated deformable convolutions to fine-tune the VGG-16 model with multi-scale receptive fields. In the experiments evaluated on the DeepLesion dataset, the modulated deformable positive-sensitive region of interest pooling model achieves the highest sensitivity score of 58.8 % on average with dilation of [4, 4, 4] and outperforms state-of-the-art models in the range of [2], [8] average false positives per image. This research demonstrates the suitability of dilation modifications and the possibility of enhancing the performance using a modulated deformable positive-sensitive region of interest pooling layer for universal lesion detectors.
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16
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Rajagopalan N, N. V, Josephraj AN, E. S. Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN. PLoS One 2021; 16:e0254180. [PMID: 34314421 PMCID: PMC8315505 DOI: 10.1371/journal.pone.0254180] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 06/21/2021] [Indexed: 12/04/2022] Open
Abstract
An efficient automatic decision support system for detection of retinal disorders is important and is the need of the hour. Optical Coherence Tomography (OCT) is the current imaging modality for the early detection of retinal disorders non-invasively. In this work, a Convolution Neural Network (CNN) model is proposed to classify three types of retinal disorders namely: Choroidal neovascularization (CNV), Drusen macular degeneration (DMD) and Diabetic macular edema (DME). The hyperparameters of the model like batch size, number of epochs, dropout rate, and the type of optimizer are tuned using random search optimization method for better performance to classify different retinal disorders. The proposed architecture provides an accuracy of 97.01%, sensitivity of 93.43%, and 98.07% specificity and it outperformed other existing models, when compared. The proposed model can be used for the large-scale screening of retinal disorders effectively.
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Affiliation(s)
- Nithya Rajagopalan
- Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India
- * E-mail:
| | - Venkateswaran N.
- Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India
| | - Alex Noel Josephraj
- Department of Electronic Engineering, College of Engineering, Shantou University, Shantou, China
| | - Srithaladevi E.
- Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India
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17
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Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci Rep 2021; 11:10930. [PMID: 34035406 PMCID: PMC8149837 DOI: 10.1038/s41598-021-90428-8] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 05/07/2021] [Indexed: 12/15/2022] Open
Abstract
Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.
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18
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Ren S, Zhan L, Chen S, Dai H, Ruan G, Li S, Liu L, Lin R, Chen H. Segmentation and Registration of the Liver in Dynamic Contrast-Enhanced Computed Tomography Images. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Dynamic contrast-enhanced computed tomography (DCE-CT) is the main auxiliary diagnostic tool for liver diseases. Liver segmentation and registration in all stages of DCE-CT images are the key technology for big data analysis of liver disease diagnosis. The change of imaging conditions
in different stages of DCE-CT brings enormous challenges to the segmentation of liver CT images. This study proposes an automatic model for liver segmentation from abdominal CT images in different stages of DCE on the basis of U-Net. The skip connection in U-Net can improve the ability of
complex feature recognition. A total of 4863 CT slices from 16 patients with hepatocellular carcinoma (HCC) were selected as the training set, and 1754 CT slices from 6 patients with HCC were selected as the test set. The training and test sets included plain scan, hepatic arterial-dominant
phase, and portal venous-dominant phase CT scans. Results showed that the Dice value of the proposed method was significantly higher than those of the full convolutional network and region-growing method. Then, 3D reconstruction and registration were performed on the segmentation results of
the liver region of DCE-CT images. The proposed method obtained the best performance, which can provide technical support for the big data analysis of liver diseases.
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Affiliation(s)
- Shuai Ren
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Ling Zhan
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Shuchao Chen
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Haitao Dai
- The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
| | - Guangying Ruan
- State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Sai Li
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Lizhi Liu
- State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Run Lin
- The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
| | - Hongbo Chen
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
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Suri JS, Agarwal S, Gupta SK, Puvvula A, Biswas M, Saba L, Bit A, Tandel GS, Agarwal M, Patrick A, Faa G, Singh IM, Oberleitner R, Turk M, Chadha PS, Johri AM, Miguel Sanches J, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Ahluwalia P, Teji J, Al-Maini M, Dhanjil SK, Sockalingam M, Saxena A, Nicolaides A, Sharma A, Rathore V, Ajuluchukwu JNA, Fatemi M, Alizad A, Viswanathan V, Krishnan PK, Naidu S. A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence. Comput Biol Med 2021; 130:104210. [PMID: 33550068 PMCID: PMC7813499 DOI: 10.1016/j.compbiomed.2021.104210] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/03/2021] [Accepted: 01/03/2021] [Indexed: 02/06/2023]
Abstract
COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.
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Affiliation(s)
- Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA.
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA, USA; Department of Computer Science Engineering, PSIT, Kanpur, India
| | - Suneet K Gupta
- Department of Computer Science Engineering, Bennett University, India
| | - Anudeep Puvvula
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA; Annu's Hospitals for Skin and Diabetes, Nellore, AP, India
| | - Mainak Biswas
- Department of Computer Science Engineering, JIS University, Kolkata, India
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Arindam Bit
- Department of Biomedical Engineering, NIT, Raipur, India
| | - Gopal S Tandel
- Department of Computer Science Engineering, VNIT, Nagpur, India
| | - Mohit Agarwal
- Department of Computer Science Engineering, Bennett University, India
| | | | - Gavino Faa
- Department of Pathology - AOU of Cagliari, Italy
| | - Inder M Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA
| | | | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany
| | - Paramjit S Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Ontario, Canada
| | - J Miguel Sanches
- Institute of Systems and Robotics, Instituto Superior Tecnico, Lisboa, Portugal
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | | | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - David W Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | | | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - George Tsoulfas
- Aristoteleion University of Thessaloniki, Thessaloniki, Greece
| | | | | | - Vikas Agarwal
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK; Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, UK
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Superspeciality Hospital, New Delhi, India
| | - Jagjit Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, USA
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, Canada
| | | | | | - Ajit Saxena
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Vijay Rathore
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA
| | | | - Mostafa Fatemi
- Dept. of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, MN, USA
| | - Azra Alizad
- Dept. of Radiology, Mayo Clinic College of Medicine and Science, MN, USA
| | - Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - P K Krishnan
- Neurology Department, Fortis Hospital, Bangalore, India
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN, USA
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20
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Zhou Y, Xu X, Song L, Wang C, Guo J, Yi Z, Li W. The application of artificial intelligence and radiomics in lung cancer. PRECISION CLINICAL MEDICINE 2020; 3:214-227. [PMID: 35694416 PMCID: PMC8982538 DOI: 10.1093/pcmedi/pbaa028] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 08/13/2020] [Accepted: 08/14/2020] [Indexed: 02/05/2023] Open
Abstract
Lung cancer is one of the most leading causes of death throughout the world, and there is an urgent requirement for the precision medical management of it. Artificial intelligence (AI) consisting of numerous advanced techniques has been widely applied in the field of medical care. Meanwhile, radiomics based on traditional machine learning also does a great job in mining information through medical images. With the integration of AI and radiomics, great progress has been made in the early diagnosis, specific characterization, and prognosis of lung cancer, which has aroused attention all over the world. In this study, we give a brief review of the current application of AI and radiomics for precision medical management in lung cancer.
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Affiliation(s)
- Yaojie Zhou
- Department of Respiratory and Critical Care Medicine, West China School of Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiuyuan Xu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Lujia Song
- West China School of Public Health, Sichuan University, Chengdu 610041, China
| | - Chengdi Wang
- Department of Respiratory and Critical Care Medicine, West China School of Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Jixiang Guo
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China School of Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
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21
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A Transfer Learning Method for Pneumonia Classification and Visualization. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10082908] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Pneumonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists.
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22
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Accurate Identification of Tomograms of Lung Nodules Using CNN: Influence of the Optimizer, Preprocessing and Segmentation. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7297567 DOI: 10.1007/978-3-030-49076-8_23] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
The diagnosis of pulmonary nodules plays an important role in the treatment of lung cancer, thus improving the diagnosis is the primary concern. This article shows a comparison of the results in the identification of computed tomography scans with pulmonary nodules, through the use of different optimizers (Adam and Nadam); the effect of the use of pre-processing and segmentation techniques using CNNs is also thoroughly explored. The dataset employed was Lung TIME which is publicly available. When no preprocessing or segmentation was applied, training accuracy above 90.24% and test accuracy above 86.8% were obtained. In contrast, when segmentation was applied without preprocessing, a training accuracy above 97.19% and test accuracy above 95.07% were reached. On the other hand, when preprocessing and segmentation was applied, a training accuracy above 96.41% and test accuracy above 94.71% were achieved. On average, the Adam optimizer scored a training accuracy of 96.17% and a test accuracy of 95.23%. Whereas, the Nadam optimizer obtained 96.25% and 95.2%, respectively. It is concluded that CNN has a good performance even when working with images with noise. The performance of the network was similar when working with preprocessing and segmentation than when using only segmentation. Also, it can be inferred that, the application of preprocessing and segmentation is an excellent option when it is required to improve accuracy in CNNs.
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