Academic Journal
أعرض في EDS

Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm

التفاصيل البيبلوغرافية
العنوان: Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm
المؤلفون: Escorcia-Gutierrez, José, Soto-Diaz, Roosvel, Madera, Natasha, Soto, Carlos, Burgos-Florez, Francisco, Rodríguez, Alexander, Mansour, Romany F.
المصدر: https://www.techscience.com/csse/v46n2/51641.
بيانات النشر: Tech Science Press
United Kingdom
سنة النشر: 2023
المجموعة: REDICUC - Repositorio Universidad de La Costa
مصطلحات موضوعية: Computer-aided diagnosis, Water strider optimization, Deep learning, Chest x-rays, Transfer learning
الوصف: Computer-aided diagnosis (CAD) models exploit artificial intelligence (AI) for chest X-ray (CXR) examination to identify the presence of tuberculosis (TB) and can improve the feasibility and performance of CXR for TB screening and triage. At the same time, CXR interpretation is a time-consuming and subjective process. Furthermore, high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis. Therefore, computer-aided diagnosis (CAD) models using machine learning (ML) and deep learning (DL) can be designed for screening TB accurately. With this motivation, this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification (WSODTL-TBC) model on Chest X-rays (CXR). The presented WSODTL-TBC model aims to detect and classify TB on CXR images. Primarily, the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation. Besides, a pre-trained residual network with a two-dimensional convolutional neural network (2D-CNN) model is applied to extract feature vectors. In addition, the WSO algorithm with long short-term memory (LSTM) model was employed for identifying and classifying TB, where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology, showing the novelty of the work. The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset, and the outcomes were investigated in many aspects. The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.
نوع الوثيقة: article in journal/newspaper
وصف الملف: 17 páginas; application/pdf
اللغة: English
تدمد: 0267-6192
Relation: Computer Systems Science and Engineering; [1] T. Rahman, A. Khandakar, M. A. Kadir, K. R. Islam, K. F. Islam et al., “Reliable tuberculosis detection using chest x-ray with deep learning, segmentation and visualization,” IEEE Access, vol. 8, pp. 191191–191586, 2020.; [2] N. Baghdadi, A. S. Maklad, A. Malki and M. A. Deif, “Reliable sarcoidosis detection using chest x-rays with efficientnets and stain-normalization techniques,” Sensors, vol. 22, no. 10, pp. 3846, 2022.; [3] L. An, K. Peng, X. Yang, P. Huang, Y. Luo et al., “E-TBNet: Light deep neural network for automatic detection of tuberculosis with x-ray dr imaging,” Sensors, vol. 22, no. 3, pp. 821, 2022.; [4] E. Showkatian, M. Salehi, H. Ghaffari, R. Reiazi, N. Sadighi et al., “Deep learning-based automatic detection of tuberculosis disease in chest X-ray images,” Polish Journal of Radiology, vol. 87, no. 1, pp. 118, 2022.; [5] T. Khatibi, A. Shahsavari and A. Farahani, “Proposing a novel multi-instance learning model for tuberculosis recognition from chest X-ray images based on CNNs, complex networks and stacked ensemble,” Physical and Engineering Sciences in Medicine, vol. 44, no. 1, pp. 291–311, 2021.; [6] A. S. Becker, C. Blüthgen, C. S. Wiltshire, B. Castelnuovo, A. Kambugu et al., “Detection of tuberculosis patterns in digital photographs of chest X-ray images using deep learning: Feasibility study,” The International Journal of Tuberculosis and Lung Disease, vol. 22, no. 3, pp. 328–335, 2018.; [7] S. Rajaraman and S. K. Antani, “Modality-specific deep learning model ensembles toward improving tb detection in chest radiographs,” IEEE Access, vol. 8, pp. 27318–27327, 2020.; [8] R. O. Panicker, K. S. Kalmady, J. Rajan and M. K. Sabu, “Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods,” Biocybernetics and Biomedical Engineering, vol. 38, no. 3, pp. 691–699, 2018.; [9] G. Tavaziva, M. Harris, S. K. Abidi, C. Geric, M. Breuninger et al., “Chest x-ray analysis with deep learningbased software as a triage test for pulmonary tuberculosis: An individual patient data meta-analysis of diagnostic accuracy,” Clinical Infectious Diseases, vol. 74, no. 8, pp. 1390–1400, 2021.; [10] J. Escorcia-Gutierrez, K. Beleño, J. Jimenez-Cabas, M. Elhoseny, M. Dahman Alshehri et al., “An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems,” Measurement, vol. 195, no. 10, pp. 111226, 2022.; [11] S. P. Kale, J. Patil, A. Kshirsagar and V. Bendre, “Early lungs tuberculosis detection using deep learning,” in Intelligent Sustainable Systems. Lecture Notes in Networks and Systems book series, vol. 333. Singapore: Springer, pp. 287–294, 2022.; [12] J. Escorcia-Gutierrez, J. Cuello, C. Barraza, M. Gamarra, P. Romero-Aroca et al., “Analysis of pre-trained convolutional neural network models in diabetic retinopathy detection through retinal fundus images,” in Int. Conf. on Computer Information Systems and Industrial Management, Barranquilla, Colombia, vol. 13293, pp. 202–213, 2022.; [13] J. Escorcia-Gutierrez, R. F. Mansour, K. Beleño, J. Jiménez-Cabas, M. Pérez et al., “Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images,” Computers, Materials and Continua, vol. 71, no. 3, pp. 4221–4235, 2022.; [14] K. Muthumayil, S. Manikandan, S. Srinivasan, J. Escorcia-Gutierrez, M. Gamarra et al., “Diagnosis of leukemia disease based on enhanced virtual neural network,” Computers, Materials and Continua, vol. 69, no. 2, pp. 2031–2044, 2021.; [15] S. Althubiti, J. Escorcia-Gutierrez, M. Gamarra, R. Soto-Diaz, R. F. Mansour et al., “Improved metaheuristics with machine learning enabled medical decision support system,” Computers, Materials and Continua, vol. 73, no. 2, pp. 2423–2439, 2022.; [16] S. Manikandan, S. Srinivasan, J. Escorcia-Gutiérrez, M. Gamarra and R. F. Mansour, “Diagnosis of leukemia disease based on enhanced virtual neural network,” Computers, Materials and Continua, vol. 69, no. 2, pp. 2031–2044, 2021.; [17] Q. H. Nguyen, B. P. Nguyen, S. D. Dao, B. Unnikrishnan, R. Dhingra et al., “Deep learning models for tuberculosis detection from chest x-ray images,” in 26th Int. Conf. on Telecommunications (ICT), Hanoi, Vietnam, pp. 381–385, 2019.; [18] S. J. Heo, Y. Kim, S. Yun, S. S. Lim, J. Kim et al., “Deep learning algorithms with demographic information help to detect tuberculosis in chest radiographs in annual workers’ health examination data,” International Journal of Environmental Research and Public Health, vol. 16, no. 2, pp. 250, 2019.; [19] M. H. A. Hijazi, S. K. T. Hwa, A. Bade, R. Yaakob and M. S. Jeffree, “Ensemble deep learning for tuberculosis detection using chest X-ray and canny edge detected images,” IAES International Journal of Artificial Intelligence, vol. 8, no. 4, pp. 429, 2019.; [20] J. Escorcia-Gutierrez, J. Torrents-Barrena, M. Gamarra, P. Romero-Aroca, A. Valls et al., “Convexity shape constraints for retinal blood vessel segmentation and foveal avascular zone detection,” Computers in Biology and Medicine, vol. 127, pp. 104049, 2020.; [21] S. Dey, R. Roychoudhury, S. Malakar and R. Sarkar, “An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images,” Applied Soft Computing, vol. 114, no. 2, pp. 108094, 2022.; [22] E. Tasci, C. Uluturk and A. Ugur, “A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection,” Neural Computing and Applications, vol. 33, no. 22, pp. 15541–15555, 2021.; [23] S. K. T. Hwa, A. Bade, M. H. A. Hijazi and M. Saffree Jeffree, “Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images,” IAES International Journal of Artificial Intelligence, vol. 9, no. 4, pp. 713, 2020.; [24] A. T. Sahlol, M. A. Elaziz, A. T. Jamal, R. Damaševičius and O. Farouk Hassan, “A novel method for detection of tuberculosis in chest radiographs using artificial ecosystem-based optimisation of deep neural network features,” Symmetry, vol. 12, no. 7, pp. 1146, 2020.; [25] C. Dasanayaka and M. B. Dissanayake, “Deep learning methods for screening pulmonary tuberculosis using chest x-rays,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 9, no. 1, pp. 39–49, 2021.; [26] A. Haq, J. Li, S. Ahmad, S. Khan, M. Alshara et al., “Diagnostic approach for accurate diagnosis of covid19 employing deep learning and transfer learning techniques through chest x-ray images clinical data in ehealthcare,” Sensors, vol. 21, no. 24, pp. 8219, 2021.; [27] R. F. Mansour, J. Escorcia-Gutierrez, M. Gamarra, D. Gupta, O. Castillo et al., “Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification,” Pattern Recognition Letters, vol. 151, no. 6, pp. 267–274, 2021.; [28] H. Okut, “Deep learning for subtyping and prediction of diseases: Long-short term memory,” in Deep Learning Applications. United Kingdom: IntechOpen, 2021.; [29] R. Karthick, A. Senthilselvi, P. Meenalochini and S. S. Pandi, “Design and analysis of linear phase finite impulse response filter using water strider optimization algorithm in FPGA,” Circuits, Systems, and Signal Processing, vol. 41, no. 9, pp. 5254–5282, 2022.; 1353; 1337; 46; J. Escorcia-Gutierrez, R. Soto-Diaz, N. Madera, C. Soto, F. Burgos-Florez et al., "Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm," Computer Systems Science and Engineering, vol. 46, no.2, pp. 1337–1353, 2023. https://doi.org/10.32604/csse.2023.035253; https://hdl.handle.net/11323/10111; Corporación Universidad de la Costa; REDICUC - Repositorio CUC; https://repositorio.cuc.edu.co/
DOI: 10.32604/csse.2023.035253
الاتاحة: https://hdl.handle.net/11323/10111
https://doi.org/10.32604/csse.2023.035253
https://repositorio.cuc.edu.co/
Rights: © 1997-2022 TSP (Henderson, USA) unless otherwise stated ; Atribución 4.0 Internacional (CC BY 4.0) ; https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess ; http://purl.org/coar/access_right/c_abf2
رقم الانضمام: edsbas.CBAB7E68
قاعدة البيانات: BASE
الوصف
تدمد:02676192
DOI:10.32604/csse.2023.035253