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Bilal A, Shafiq M, Fang F, Waqar M, Ullah I, Ghadi YY, Long H, Zeng R. IGWO-IVNet3: DL-Based Automatic Diagnosis of Lung Nodules Using an Improved Gray Wolf Optimization and InceptionNet-V3. SENSORS (BASEL, SWITZERLAND) 2022; 22:9603. [PMID: 36559970 PMCID: PMC9786099 DOI: 10.3390/s22249603] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 11/28/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
Artificial intelligence plays an essential role in diagnosing lung cancer. Lung cancer is notoriously difficult to diagnose until it has progressed to a late stage, making it a leading cause of cancer-related mortality. Lung cancer is fatal if not treated early, making this a significant issue. Initial diagnosis of malignant nodules is often made using chest radiography (X-ray) and computed tomography (CT) scans; nevertheless, the possibility of benign nodules leads to wrong choices. In their first phases, benign and malignant nodules seem very similar. Additionally, radiologists have a hard time viewing and categorizing lung abnormalities. Lung cancer screenings performed by radiologists are often performed with the use of computer-aided diagnostic technologies. Computer scientists have presented many methods for identifying lung cancer in recent years. Low-quality images compromise the segmentation process, rendering traditional lung cancer prediction algorithms inaccurate. This article suggests a highly effective strategy for identifying and categorizing lung cancer. Noise in the pictures was reduced using a weighted filter, and the improved Gray Wolf Optimization method was performed before segmentation with watershed modification and dilation operations. We used InceptionNet-V3 to classify lung cancer into three groups, and it performed well compared to prior studies: 98.96% accuracy, 94.74% specificity, as well as 100% sensitivity.
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Affiliation(s)
- Anas Bilal
- College of Information Science and Technology, Hainan Normal University, Haikou 571158, China
| | - Muhammad Shafiq
- School of Information Engineering, Qujing Normal University, Qujing 655011, China
| | - Fang Fang
- College of Information Engineering, Hainan Vocational University of Science and Technology, Haikou 571126, China
| | - Muhammad Waqar
- Department of Computer Science, COMSATS University, Islamabad 45550, Pakistan
| | - Inam Ullah
- BK21 Chungbuk Information Technology Education and Research Center, Chungbuk National University, Cheongju-si 28644, Republic of Korea
| | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Abu Dhabi 64141, United Arab Emirates
| | - Haixia Long
- College of Information Science and Technology, Hainan Normal University, Haikou 571158, China
| | - Rao Zeng
- College of Information Science and Technology, Hainan Normal University, Haikou 571158, China
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Moragheb MA, Badie A, Noshad A. An Effective Approach for Automated Lung Node Detection using CT Scans. J Biomed Phys Eng 2022; 12:377-386. [PMID: 36059280 PMCID: PMC9395629 DOI: 10.31661/jbpe.v0i0.2110-1412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 05/20/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Pulmonary or benign nodules are classified as nodules with a diameter of 3 cm or less and defined as non-cancerous nodules. The early diagnosis of malignant lung nodules is important for a more reliable prognosis of lung cancer and less invasive chemotherapy and radiotherapy procedures. OBJECTIVE This study aimed to introduce an improved hybrid approach for efficient nodule mask generation and false-positive reduction. MATERIAL AND METHODS In this experimental study, nodule segmentation preprocessing was conducted to prepare the input computed tomography (CT) scans for the U-Net convolutional neural network (CNN) model, and includes the normalization of CT scans and transfer of pixel values corresponding to the radiodensity of Hounsfield Units (HU). A U-Net CNN was developed based on lung CT scans for nodule identification. RESULTS The U-net model converged to a dice coefficient of 0.678 with a sensitivity of 75%. Many false positives were considered in every real positive, at 11.1, reduced in the proposed CNN to 2.32 FPs (False Positive) per TP (True Positive). CONCLUSION Based on the disadvantages of the largest nodule, the similarity of extracted features of the current study with those of others was imperative. The improved hybrid approach introduced was useful for other image classification tasks as expected.
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Affiliation(s)
- Mohammad Amin Moragheb
- MSc, Department of Computer Engineering, Faculty of Engineering, Mamasani Higher Education Center, Mamasani, Iran
| | - Ali Badie
- MSc, Department of Computer Engineering, Faculty of Engineering, Salman Farsi University of Kazerun, Kazerun, Iran
| | - Ali Noshad
- BSc, Department of Computer Engineering, Salman Farsi University of Kazerun, Kazerun, Iran
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Ramana K, Kumar MR, Sreenivasulu K, Gadekallu TR, Bhatia S, Agarwal P, Idrees SM. Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks. Front Oncol 2022; 12:886739. [PMID: 35785184 PMCID: PMC9247339 DOI: 10.3389/fonc.2022.886739] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/13/2022] [Indexed: 12/12/2022] Open
Abstract
Lung cancer is the cellular fission of abnormal cells inside the lungs that leads to 72% of total deaths worldwide. Lung cancer are also recognized to be one of the leading causes of mortality, with a chance of survival of only 19%. Tumors can be diagnosed using a variety of procedures, including X-rays, CT scans, biopsies, and PET-CT scans. From the above techniques, Computer Tomography (CT) scan technique is considered to be one of the most powerful tools for an early diagnosis of lung cancers. Recently, machine and deep learning algorithms have picked up peak energy, and this aids in building a strong diagnosis and prediction system using CT scan images. But achieving the best performances in diagnosis still remains on the darker side of the research. To solve this problem, this paper proposes novel saliency-based capsule networks for better segmentation and employs the optimized pre-trained transfer learning for the better prediction of lung cancers from the input CT images. The integration of capsule-based saliency segmentation leads to the reduction and eventually reduces the risk of computational complexity and overfitting problem. Additionally, hyperparameters of pretrained networks are tuned by the whale optimization algorithm to improve the prediction accuracy by sacrificing the complexity. The extensive experimentation carried out using the LUNA-16 and LIDC Lung Image datasets and various performance metrics such as accuracy, precision, recall, specificity, and F1-score are evaluated and analyzed. Experimental results demonstrate that the proposed framework has achieved the peak performance of 98.5% accuracy, 99.0% precision, 98.8% recall, and 99.1% F1-score and outperformed the DenseNet, AlexNet, Resnets-50, Resnets-100, VGG-16, and Inception models.
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Affiliation(s)
- Kadiyala Ramana
- Department of Information Technology (IT), Chaitanya Bharathi Institute of Technology, Hyderabad, India
| | - Madapuri Rudra Kumar
- Department of Computer Science and Engineering (CSE), G. Pullaiah College of Engineering and Technology, Kurnool, India
| | - K. Sreenivasulu
- Department of Computer Science and Engineering (CSE), G. Pullaiah College of Engineering and Technology, Kurnool, India
| | | | - Surbhi Bhatia
- Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Hasa, Saudi Arabia
| | - Parul Agarwal
- Department of Computer Science and Engineering (CSE), Jamia Hamdard, India
| | - Sheikh Mohammad Idrees
- Department of Computer Science Institutt for datateknologi og informatikk (IDI), Norwegian University of Science and Technology, Gjøvik, Norway
- *Correspondence: Sheikh Mohammad Idrees,
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da Silva GLF, Valente TLA, Silva AC, de Paiva AC, Gattass M. Convolutional neural network-based PSO for lung nodule false positive reduction on CT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 162:109-118. [PMID: 29903476 DOI: 10.1016/j.cmpb.2018.05.006] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 09/15/2017] [Accepted: 05/03/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Detection of lung nodules is critical in CAD systems; this is because of their similar contrast with other structures and low density, which result in the generation of numerous false positives (FPs). Therefore, this study proposes a methodology to reduce the FP number using a deep learning technique in conjunction with an evolutionary technique. METHOD The particle swarm optimization (PSO) algorithm was used to optimize the network hyperparameters in the convolutional neural network (CNN) in order to enhance the network performance and eliminate the requirement of manual search. RESULTS The methodology was tested on computed tomography (CT) scans from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) with the highest accuracy of 97.62%, sensitivity of 92.20%, specificity of 98.64%, and area under the receiver operating characteristic (ROC) curve of 0.955. CONCLUSION The results demonstrate the high performance-potential of the PSO algorithm in the identification of optimal CNN hyperparameters for lung nodule candidate classification into nodules and non-nodules, increasing the sensitivity rates in the FP reduction step of CAD systems.
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Affiliation(s)
- Giovanni Lucca França da Silva
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, São Luís, MA 65085-580, Brazil.
| | - Thales Levi Azevedo Valente
- Pontifical Catholic University of Rio de Janeiro - PUC - Rio R. São Vicente, 225, Gávea, Rio de Janeiro, RJ 22453-900, Brazil.
| | - Aristófanes Corrêa Silva
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, São Luís, MA 65085-580, Brazil.
| | - Anselmo Cardoso de Paiva
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, São Luís, MA 65085-580, Brazil.
| | - Marcelo Gattass
- Pontifical Catholic University of Rio de Janeiro - PUC - Rio R. São Vicente, 225, Gávea, Rio de Janeiro, RJ 22453-900, Brazil.
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Causey JL, Zhang J, Ma S, Jiang B, Qualls JA, Politte DG, Prior F, Zhang S, Huang X. Highly accurate model for prediction of lung nodule malignancy with CT scans. Sci Rep 2018; 8:9286. [PMID: 29915334 PMCID: PMC6006355 DOI: 10.1038/s41598-018-27569-w] [Citation(s) in RCA: 104] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 06/04/2018] [Indexed: 11/26/2022] Open
Abstract
Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX .
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Affiliation(s)
- Jason L Causey
- Department of Computer Science, Arkansas State University, Jonesboro, Arkansas, 72467, United States of America
- The UALR/UAMS Joint Graduate Program in Bioinformatics, Little Rock, Arkansas, 72204, United States of America
| | - Junyu Zhang
- Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota, 55455, United States of America
| | - Shiqian Ma
- Department of Mathematics, University of California, Davis, California, 95616, United States of America
| | - Bo Jiang
- Research Center for Management Science and Data Analytics, School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, China
| | - Jake A Qualls
- Department of Computer Science, Arkansas State University, Jonesboro, Arkansas, 72467, United States of America
- The UALR/UAMS Joint Graduate Program in Bioinformatics, Little Rock, Arkansas, 72204, United States of America
| | - David G Politte
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri, 63110, United States of America
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, 72205, United States of America.
| | - Shuzhong Zhang
- Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota, 55455, United States of America.
| | - Xiuzhen Huang
- Department of Computer Science, Arkansas State University, Jonesboro, Arkansas, 72467, United States of America.
- The UALR/UAMS Joint Graduate Program in Bioinformatics, Little Rock, Arkansas, 72204, United States of America.
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Classification of malignant and benign lung nodules using taxonomic diversity index and phylogenetic distance. Med Biol Eng Comput 2018; 56:2125-2136. [DOI: 10.1007/s11517-018-1841-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 04/23/2018] [Indexed: 10/16/2022]
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