Optimal synergic deep learning for COVID-19 classification using chest x-ray images
A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological inve...
- Autores:
-
Escorcia-Gutierrez, José
Gamarra, Margarita
Soto-Diaz, Roosvel
alsafari, safa
Yafoz, Ayman
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:11323/10601
- Acceso en línea:
- https://hdl.handle.net/11323/10601
https://repositorio.cuc.edu.co/
- Palabra clave:
- Artificial intelligence
Chest X-ray
COVID-19
Optimized synergic deep learning
Preprocessing
Public health
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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Optimal synergic deep learning for COVID-19 classification using chest x-ray images |
| title |
Optimal synergic deep learning for COVID-19 classification using chest x-ray images |
| spellingShingle |
Optimal synergic deep learning for COVID-19 classification using chest x-ray images Artificial intelligence Chest X-ray COVID-19 Optimized synergic deep learning Preprocessing Public health |
| title_short |
Optimal synergic deep learning for COVID-19 classification using chest x-ray images |
| title_full |
Optimal synergic deep learning for COVID-19 classification using chest x-ray images |
| title_fullStr |
Optimal synergic deep learning for COVID-19 classification using chest x-ray images |
| title_full_unstemmed |
Optimal synergic deep learning for COVID-19 classification using chest x-ray images |
| title_sort |
Optimal synergic deep learning for COVID-19 classification using chest x-ray images |
| dc.creator.fl_str_mv |
Escorcia-Gutierrez, José Gamarra, Margarita Soto-Diaz, Roosvel alsafari, safa Yafoz, Ayman Mansour, Romany F. |
| dc.contributor.author.none.fl_str_mv |
Escorcia-Gutierrez, José Gamarra, Margarita Soto-Diaz, Roosvel alsafari, safa Yafoz, Ayman Mansour, Romany F. |
| dc.subject.proposal.eng.fl_str_mv |
Artificial intelligence Chest X-ray COVID-19 Optimized synergic deep learning Preprocessing Public health |
| topic |
Artificial intelligence Chest X-ray COVID-19 Optimized synergic deep learning Preprocessing Public health |
| description |
A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This method is inclusive of preprocessing and classification based on optimization. The data is preprocessed using Gaussian filtering (GF) to remove any extraneous noise from the image’s edges. Then, the OSDL model is applied to classify the CXRs under different severity levels based on CXR data. The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work. OSDL model, applied in this study, was validated using the COVID-19 dataset. The experiments were conducted upon the proposed OSDL model, which achieved a classification accuracy of 99.83%, while the current Convolutional Neural Network achieved less classification accuracy, i.e., 98.14%. |
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2023 |
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2023-11-20T15:43:02Z |
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2023-11-20T15:43:02Z |
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2023-04-29 |
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Artículo de revista |
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J. Escorcia-Gutierrez, M. Gamarra, R. Soto-Diaz, S. Alsafari, A. Yafoz et al., "Optimal synergic deep learning for covid-19 classification using chest x-ray images," Computers, Materials & Continua, vol. 75, no.3, pp. 5255–5270, 2023. https://doi.org/10.32604/cmc.2023.033731 |
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1546-2218 |
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https://hdl.handle.net/11323/10601 |
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10.32604/cmc.2023.033731 |
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1546-2226 |
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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, M. Gamarra, R. Soto-Diaz, S. Alsafari, A. Yafoz et al., "Optimal synergic deep learning for covid-19 classification using chest x-ray images," Computers, Materials & Continua, vol. 75, no.3, pp. 5255–5270, 2023. https://doi.org/10.32604/cmc.2023.033731 1546-2218 10.32604/cmc.2023.033731 1546-2226 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/10601 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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Computers, Materials and Continua |
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[1] S. Sadhana, S. Pandiarajan, E. Sivaraman and D. Daniel, “AI-based power screening solution for SARSCOV2 infection: A sociodemographic survey and COVID-19 cough detector,”Procedia Computer Science, vol. 194, no. 9, pp. 255–271, 2021. [2] E. Mahase, “Coronavirus: COVID-19 has killed more people than SARS and MERS combined, despite lower case fatality rate,” BMJ, vol. 368, pp. m641, 2020. [3] U. Rani and R. K. Dhir, “Platform work and the COVID-19 pandemic,” The Indian Journal of Labour Economics, vol. 63, no. S1, pp. 163–171, 2020. [4] M. Ahmadi, A. Sharifi, S. Dorosti, S. J. Ghoushchi and N. Ghanbari, “Investigation of effective climatology parameters on COVID-19 outbreak in Iran,” Science of the Total Environment, vol. 729, no. 8, pp. 138705, 2020. [5] Y. Fang, H. Zhang, J. Xie, M. Lin, L. Ying et al., “Sensitivity of chest CT for COVID-19: Comparison to RT-PCR,” Radiology, vol. 296, no. 2, pp. E115–E117, 2020. [6] M. N. Ikeda, K. Imai, S. Tabata, K. Miyoshi, N. Murahara et al., “Clinical evaluation of self-collected saliva by quantitative reverse transcription-PCR (RT-qPCR), direct RT-qPCR, reverse transcription-loopmediated isothermal amplification, and a rapid antigen test to diagnose COVID-19,” Journal of Clinical Microbiology, vol. 58, no. 9, pp. e01438-20, 2020. [7] M. L. Bastos, G. Tavaziva, S. K. Abidi, J. R. Campbell, L. P. Haraoui et al., “Diagnostic accuracy of serological tests for COVID-19: Systematic review and meta-analysis,” BMJ, vol. 370, pp. 1–13, 2020. [8] M. Rahimzadeh, A. Attar and S. M. Sakhaei, “A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset,”Biomedical Signal Processing and Control, vol. 68, no. 1, pp. 102588, 2021. [9] D. Li, D. Wang, J. Dong, N. Wang, H. Huang et al., “False-negative results of real-time reverse-transcriptase polymerase chain reaction for severe acute respiratory syndrome coronavirus 2: Role of deep-learning-based CT diagnosis and insights from two cases,” Korean Journal of Radiology, vol. 21, no. 4, pp. 505, 2020. [10] F. Shi, J. Wang, J. Shi, Z. Wu, Q. Wang et al., “Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19,” IEEE Reviews in Biomedical Engineering, vol. 14, pp. 4–15, 2020. [11] G. Wang, X. Liu, C. Li, Z. Xu, J. Ruan et al., “A noise-robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2653–2663, 2020. [12] A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, A. Mohammadi et al., “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks,” Computers in Biology and Medicine, vol. 121, no. 10229, pp. 103795, 2020. [13] L. Zhou, Z. Li, J. Zhou, H. Li, Y. Chen et al., “A rapid, accurate and machine-agnostic segmentation and quantification method for CT-based COVID-19 diagnosis,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2638–2652, 2020. [14] R. Ranjbarzadeh and S. B. Saadi, “Automated liver and tumor segmentation based on concave and convex points using fuzzy C-means and mean shift clustering,” Measurement, vol. 150, no. 2, pp. 107086, 2020. [15] X. Ouyang, J. Huo, L. Xia, F. Shan, J. Liu et al., “Dual-sampling attention network for diagnosis of COVID19 from community acquired pneumonia,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2595– 2605, 2020. [16] V. Rajinikanth, N. Dey, A. N. J. Raj, A. E. Hassanien, K. C. Santosh et al., “Harmony-search and otsu based system for coronavirus disease (COVID-19) detection using lung CT scan images,” arXiv preprint arXiv:2004.03431, 2004. [17] S. Minaee, R. Kafieh, M. Sonka, S. Yazdani, G. J. Soufi et al., “Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning,”Medical Image Analysis, vol. 65, no. 12, pp. 101794, 2020. [18] D. P. Fan, T. Zhou, G. P. Ji, Y. Zhou, G. Chen et al., “Inf-Net: Automatic COVID-19 lung infection segmentation from CT images,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2626–2637, 2020. [19] X. Wang, X. Deng, Q. Fu, Q. Zhou, J. Feng et al., “A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2615–2625, 2020. [20] M. Barstugan, U. Ozkaya and S. Ozturk, “Coronavirus (COVID-19) classification using CT images by machine learning methods,” arXiv preprint arXiv:2003.09424, 2020. [21] H. Panwar, P. K. Gupta, M. K. Siddiqui, R. M. Menendez, V. Singh et al., “Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet,” Chaos, Solitons & Fractals, vol. 128, no. 3, pp. 109944, 2020. [22] S. Toraman, T. B. Alakus and I. Turkoglu, “Convolutional CapsNet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks,” Chaos, Solitons & Fractals, vol. 140, no. 18, pp. 110122, 2020. [23] M. Nour, Z. Cömert and K. Polat, “A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization,”Applied Soft Computing, vol. 97, no. Part A, pp. 106580, 2020. [24] 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. 151, pp. 267–274, 2021. [25] S. Ahuja, B. K. Panigrahi, N. Dey, V. Rajinikanth, T. K. Gandhi et al., “Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices,” Applied Intelligence, vol. 51, no. 1, pp. 571– 585, 2021. [26] T. Kaur, T. K. Gandhi and B. K. Panigrahi, “Automated diagnosis of COVID-19 using deep features and parameter free BAT optimization,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 9, pp. 1–9, 2021. [27] K. K. Singh and A. Singh, “Diagnosis of COVID-19 from chest X-ray images using wavelets-based depthwise convolution network,” Big Data Mining and Analytics, vol. 4, no. 2, pp. 84–93, 2021. [28] A. Shamsi, H. Asgharnezhad, S. S. Jokandan, A. Khosravi, P. M. Kebria et al., “An uncertainty-aware transfer learning-based framework for COVID-19 diagnosis,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 4, pp. 1408–1417, 2021. [29] Y. H. Wu, S. H. Gao, J. Mei, J. Xu, D. P. Fan et al., “JCS: An explainable COVID-19 diagnosis system by joint classification and segmentation,” IEEE Transactions on Image Processing, vol. 30, pp. 3113–3126, 2021. [30] M. Ragab, S. Alshehri, N. A. Alhakamy, W. Alsaggaf, H. A. Alhadrami et al., “Machine learning with quantum seagull optimization model for COVID-19 chest X-ray image classification,” Journal of Healthcare Engineering, vol. 2022, no. 1, pp. 1–13, 2022. [31] K. Shankar and E. Perumal, “A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images,” Complex & Intelligent Systems, vol. 7, no. 3, pp. 1277–1293, 2020. [32] D. Nandan, J. Kanungo and A. Mahajan, “An error-efficient Gaussian filter for image processing by using the expanded operand decomposition logarithm multiplication,” Journal of Ambient Intelligence and Humanized Computing, 2018. https://doi.org/10.1007/s12652-018-0933-x [33] K. Shankar, E. Perumal, M. Elhoseny, F. Taher, B. B. Gupta et al., “Synergic deep learning for smart health diagnosis of COVID-19 for connected living and smart cities,” ACM Transactions on Internet Technology, vol. 22, no. 3, pp. 1–14, 2022. [34] K. Shankar, E. Perumal, V. G. Díaz, P. Tiwari, D. Gupta et al., “An optimal cascaded recurrent neural network for intelligent COVID-19 detection using chest X-ray images,” Applied Soft Computing, vol. 113, no. Part A, pp. 1–13, 2021. [35] C. S. S. Anupama, M. Sivaram, E. L. Lydia, D. Gupta and K. Shankar, “Synergic deep learning model-based automated detection and classification of brain intracranial hemorrhage images in wearable networks,” Personal and Ubiquitous Computing, 2020. https://doi.org/10.1007/s00779-020-01492-2 [36] H. Jia, X. Peng and C. Lang, “Remora optimization algorithm,” Expert Systems with Applications, vol. 185, no. 2, pp. 115665, 2021. |
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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, JoséGamarra, MargaritaSoto-Diaz, Roosvelalsafari, safaYafoz, AymanMansour, Romany F.2023-11-20T15:43:02Z2023-11-20T15:43:02Z2023-04-29J. Escorcia-Gutierrez, M. Gamarra, R. Soto-Diaz, S. Alsafari, A. Yafoz et al., "Optimal synergic deep learning for covid-19 classification using chest x-ray images," Computers, Materials & Continua, vol. 75, no.3, pp. 5255–5270, 2023. https://doi.org/10.32604/cmc.2023.0337311546-2218https://hdl.handle.net/11323/1060110.32604/cmc.2023.0337311546-2226Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This method is inclusive of preprocessing and classification based on optimization. The data is preprocessed using Gaussian filtering (GF) to remove any extraneous noise from the image’s edges. Then, the OSDL model is applied to classify the CXRs under different severity levels based on CXR data. The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work. OSDL model, applied in this study, was validated using the COVID-19 dataset. The experiments were conducted upon the proposed OSDL model, which achieved a classification accuracy of 99.83%, while the current Convolutional Neural Network achieved less classification accuracy, i.e., 98.14%.16 páginasapplication/pdfengTech Science PressUnited Stateshttps://www.techscience.com/cmc/v75n3/52554Optimal synergic deep learning for COVID-19 classification using chest x-ray imagesArtículo 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_970fb48d4fbd8a85Computers, Materials and Continua[1] S. Sadhana, S. Pandiarajan, E. Sivaraman and D. Daniel, “AI-based power screening solution for SARSCOV2 infection: A sociodemographic survey and COVID-19 cough detector,”Procedia Computer Science, vol. 194, no. 9, pp. 255–271, 2021.[2] E. Mahase, “Coronavirus: COVID-19 has killed more people than SARS and MERS combined, despite lower case fatality rate,” BMJ, vol. 368, pp. m641, 2020.[3] U. Rani and R. K. Dhir, “Platform work and the COVID-19 pandemic,” The Indian Journal of Labour Economics, vol. 63, no. S1, pp. 163–171, 2020.[4] M. Ahmadi, A. Sharifi, S. Dorosti, S. J. Ghoushchi and N. Ghanbari, “Investigation of effective climatology parameters on COVID-19 outbreak in Iran,” Science of the Total Environment, vol. 729, no. 8, pp. 138705, 2020.[5] Y. Fang, H. Zhang, J. Xie, M. Lin, L. Ying et al., “Sensitivity of chest CT for COVID-19: Comparison to RT-PCR,” Radiology, vol. 296, no. 2, pp. E115–E117, 2020.[6] M. N. Ikeda, K. Imai, S. Tabata, K. Miyoshi, N. Murahara et al., “Clinical evaluation of self-collected saliva by quantitative reverse transcription-PCR (RT-qPCR), direct RT-qPCR, reverse transcription-loopmediated isothermal amplification, and a rapid antigen test to diagnose COVID-19,” Journal of Clinical Microbiology, vol. 58, no. 9, pp. e01438-20, 2020.[7] M. L. Bastos, G. Tavaziva, S. K. Abidi, J. R. Campbell, L. P. Haraoui et al., “Diagnostic accuracy of serological tests for COVID-19: Systematic review and meta-analysis,” BMJ, vol. 370, pp. 1–13, 2020.[8] M. Rahimzadeh, A. Attar and S. M. Sakhaei, “A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset,”Biomedical Signal Processing and Control, vol. 68, no. 1, pp. 102588, 2021.[9] D. Li, D. Wang, J. Dong, N. Wang, H. Huang et al., “False-negative results of real-time reverse-transcriptase polymerase chain reaction for severe acute respiratory syndrome coronavirus 2: Role of deep-learning-based CT diagnosis and insights from two cases,” Korean Journal of Radiology, vol. 21, no. 4, pp. 505, 2020.[10] F. Shi, J. Wang, J. Shi, Z. Wu, Q. Wang et al., “Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19,” IEEE Reviews in Biomedical Engineering, vol. 14, pp. 4–15, 2020.[11] G. Wang, X. Liu, C. Li, Z. Xu, J. Ruan et al., “A noise-robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2653–2663, 2020.[12] A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, A. Mohammadi et al., “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks,” Computers in Biology and Medicine, vol. 121, no. 10229, pp. 103795, 2020.[13] L. Zhou, Z. Li, J. Zhou, H. Li, Y. Chen et al., “A rapid, accurate and machine-agnostic segmentation and quantification method for CT-based COVID-19 diagnosis,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2638–2652, 2020.[14] R. Ranjbarzadeh and S. B. Saadi, “Automated liver and tumor segmentation based on concave and convex points using fuzzy C-means and mean shift clustering,” Measurement, vol. 150, no. 2, pp. 107086, 2020.[15] X. Ouyang, J. Huo, L. Xia, F. Shan, J. Liu et al., “Dual-sampling attention network for diagnosis of COVID19 from community acquired pneumonia,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2595– 2605, 2020.[16] V. Rajinikanth, N. Dey, A. N. J. Raj, A. E. Hassanien, K. C. Santosh et al., “Harmony-search and otsu based system for coronavirus disease (COVID-19) detection using lung CT scan images,” arXiv preprint arXiv:2004.03431, 2004.[17] S. Minaee, R. Kafieh, M. Sonka, S. Yazdani, G. J. Soufi et al., “Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning,”Medical Image Analysis, vol. 65, no. 12, pp. 101794, 2020.[18] D. P. Fan, T. Zhou, G. P. Ji, Y. Zhou, G. Chen et al., “Inf-Net: Automatic COVID-19 lung infection segmentation from CT images,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2626–2637, 2020.[19] X. Wang, X. Deng, Q. Fu, Q. Zhou, J. Feng et al., “A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT,” IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2615–2625, 2020.[20] M. Barstugan, U. Ozkaya and S. Ozturk, “Coronavirus (COVID-19) classification using CT images by machine learning methods,” arXiv preprint arXiv:2003.09424, 2020.[21] H. 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Lang, “Remora optimization algorithm,” Expert Systems with Applications, vol. 185, no. 2, pp. 115665, 2021.52705255375Artificial intelligenceChest X-rayCOVID-19Optimized synergic deep learningPreprocessingPublic healthPublicationORIGINALOptimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images.pdfOptimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images.pdfArtículoapplication/pdf1534543https://repositorio.cuc.edu.co/bitstreams/a10e9857-a82f-47f0-8dc5-a54120f706f3/download370f9e0e425f23c73ee9257673b814f6MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/e596711a-aeab-4a6f-bbdc-a5bb122df6e4/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTOptimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images.pdf.txtOptimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images.pdf.txtExtracted 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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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