Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm
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...
- Autores:
-
Escorcia-Gutierrez, José
Soto-Diaz, Roosvel
Madera, Natasha
Soto, Carlos
Burgos-Florez, Francisco
Rodríguez, Alexander
Mansour, Romany F.
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/10111
- Acceso en línea:
- https://hdl.handle.net/11323/10111
https://repositorio.cuc.edu.co/
- Palabra clave:
- Computer-aided diagnosis
Water strider optimization
Deep learning
Chest x-rays
Transfer learning
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm |
| title |
Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm |
| spellingShingle |
Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm Computer-aided diagnosis Water strider optimization Deep learning Chest x-rays Transfer learning |
| title_short |
Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm |
| title_full |
Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm |
| title_fullStr |
Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm |
| title_full_unstemmed |
Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm |
| title_sort |
Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm |
| dc.creator.fl_str_mv |
Escorcia-Gutierrez, José Soto-Diaz, Roosvel Madera, Natasha Soto, Carlos Burgos-Florez, Francisco Rodríguez, Alexander Mansour, Romany F. |
| dc.contributor.author.none.fl_str_mv |
Escorcia-Gutierrez, José Soto-Diaz, Roosvel Madera, Natasha Soto, Carlos Burgos-Florez, Francisco Rodríguez, Alexander Mansour, Romany F. |
| dc.subject.proposal.eng.fl_str_mv |
Computer-aided diagnosis Water strider optimization Deep learning Chest x-rays Transfer learning |
| topic |
Computer-aided diagnosis Water strider optimization Deep learning Chest x-rays Transfer learning |
| description |
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. |
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2023 |
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2023-05-12T21:52:43Z |
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2023-05-12T21:52:43Z |
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2023 |
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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 |
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0267-6192 |
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https://hdl.handle.net/11323/10111 |
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10.32604/csse.2023.035253 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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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 0267-6192 10.32604/csse.2023.035253 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/10111 https://repositorio.cuc.edu.co/ |
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eng |
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Computer Systems Science and Engineering |
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[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. |
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Atribución 4.0 Internacional (CC BY 4.0)© 1997-2022 TSP (Henderson, USA) unless otherwise statedhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Escorcia-Gutierrez, JoséSoto-Diaz, RoosvelMadera, NatashaSoto, CarlosBurgos-Florez, FranciscoRodríguez, AlexanderMansour, Romany F.2023-05-12T21:52:43Z2023-05-12T21:52:43Z2023J. 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.2023.0352530267-6192https://hdl.handle.net/11323/1011110.32604/csse.2023.035253Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.17 páginasapplication/pdfengTech Science PressUnited Kingdomhttps://www.techscience.com/csse/v46n2/51641Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithmArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Computer 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. 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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, 2022.13531337246Computer-aided diagnosisWater strider optimizationDeep learningChest x-raysTransfer learningPublicationORIGINALComputer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm.pdfComputer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm.pdfapplication/pdf4670358https://repositorio.cuc.edu.co/bitstreams/c2895577-241b-4112-bbd1-14c4e62217ad/downloadba5474d1270ac5c5cd560e39e87b7f01MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/b072c44f-3157-4397-b012-6093fc3b596b/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTComputer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm.pdf.txtComputer-Aided Diagnosis for 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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