{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:47:14Z","timestamp":1750308434608,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1145\/3454127.3457615","type":"proceedings-article","created":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T05:13:07Z","timestamp":1637989987000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Detection of COVID-19 from Chest X-ray images using Deep learning"],"prefix":"10.1145","author":[{"given":"Insaf","family":"Bellamine","sequence":"first","affiliation":[{"name":"Faculty of Sciences chouaib Doukkali University, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hakim","family":"Nasaoui","sequence":"additional","affiliation":[{"name":"Faculty of Sciences chouaib Doukkali University, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hassan","family":"Silkan","sequence":"additional","affiliation":[{"name":"Faculty of Sciences chouaib Doukkali University, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,11,26]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2008-3"},{"key":"e_1_3_2_1_2_1","volume-title":"Response, Disease Outbreak News","author":"World Health Organization","year":"2020","unstructured":"World Health Organization , Pneumonia of Unknown Cause\u2013China. Emergencies Preparedness , Response, Disease Outbreak News , World Health Organization (WHO) , 2020 . World Health Organization, Pneumonia of Unknown Cause\u2013China. Emergencies Preparedness, Response, Disease Outbreak News, World Health Organization (WHO), 2020."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2001191"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020200028"},{"key":"e_1_3_2_1_5_1","first-page":"1","article-title":"A review of coronavirus disease-2019 (COVID-19)","author":"Tanu SINGHAL","year":"2020","unstructured":"SINGHAL , Tanu . A review of coronavirus disease-2019 (COVID-19) . The Indian Journal of Pediatrics , 2020 , p. 1 - 6 . SINGHAL, Tanu. A review of coronavirus disease-2019 (COVID-19). The Indian Journal of Pediatrics, 2020, p. 1-6.","journal-title":"The Indian Journal of Pediatrics"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","first-page":"200463","DOI":"10.1148\/radiol.2020200463","article-title":"CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection","author":"Adam BERNHEIM","year":"2020","unstructured":"BERNHEIM , Adam , MEI , Xueyan , HUANG , Mingqian , Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection . Radiology , 2020 , p. 200463 . BERNHEIM, Adam, MEI, Xueyan, HUANG, Mingqian, Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology, 2020, p. 200463.","journal-title":"Radiology"},{"key":"e_1_3_2_1_7_1","volume-title":"Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT?. European journal of radiology","author":"Chunqin LONG","year":"2020","unstructured":"LONG , Chunqin , XU , Huaxiang , SHEN , Qinglin , Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT?. European journal of radiology , 2020 , p. 108961. LONG, Chunqin, XU, Huaxiang, SHEN, Qinglin, Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT?. European journal of radiology, 2020, p. 108961."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(20)30134-1"},{"key":"e_1_3_2_1_9_1","volume-title":"The Lancet Infectious Diseases","author":"Heshui SHI","year":"2020","unstructured":"SHI , Heshui , HAN , Xiaoyu , JIANG , Nanchuan , Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study . The Lancet Infectious Diseases , 2020 . SHI, Heshui, HAN, Xiaoyu, JIANG, Nanchuan, Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. The Lancet Infectious Diseases, 2020."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.20.22954"},{"key":"e_1_3_2_1_11_1","volume-title":"Radiology","author":"Zu Z.Y.","year":"2020","unstructured":"Z.Y. Zu , M.D. Jiang , P.P. Xu , W. Chen , Q.Q. Ni , G.M. Lu , L.J. Zhang , Coronavirus disease 2019 (COVID-19): a perspective from China , Radiology ( 2020 ), https:\/\/doi. org\/10.1148\/radiol.2020200490. Z.Y. Zu, M.D. Jiang, P.P. Xu, W. Chen, Q.Q. Ni, G.M. Lu, L.J. Zhang, Coronavirus disease 2019 (COVID-19): a perspective from China, Radiology (2020), https:\/\/doi. org\/10.1148\/radiol.2020200490."},{"key":"e_1_3_2_1_12_1","volume-title":"Dermatologist-level classification of skin cancer with deep neural networks. nature","author":"Andre ESTEVA","year":"2017","unstructured":"ESTEVA , Andre , KUPREL , Brett , NOVOA , Roberto A. , Dermatologist-level classification of skin cancer with deep neural networks. nature , 2017 , vol. 542 , no 7639, p. 115-118. ESTEVA, Andre, KUPREL, Brett, NOVOA, Roberto A., Dermatologist-level classification of skin cancer with deep neural networks. nature, 2017, vol. 542, no 7639, p. 115-118."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1147\/jrd.2017.2708299"},{"key":"e_1_3_2_1_14_1","volume-title":"ZHU","author":"Pranav RAJPURKAR","year":"2017","unstructured":"RAJPURKAR , Pranav , IRVIN , Jeremy , ZHU , Kaylie, Chexnet : Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225, 2017 . RAJPURKAR, Pranav, IRVIN, Jeremy, ZHU, Kaylie, Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225, 2017."},{"key":"e_1_3_2_1_15_1","volume-title":"Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19. arXiv preprint arXiv:2004.03399","author":"Karim HAMMOUDI","year":"2020","unstructured":"HAMMOUDI , Karim , BENHABILES , Halim , MELKEMI , Mahmoud , Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19. arXiv preprint arXiv:2004.03399 , 2020 . HAMMOUDI, Karim, BENHABILES, Halim, MELKEMI, Mahmoud, Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19. arXiv preprint arXiv:2004.03399, 2020."},{"key":"e_1_3_2_1_16_1","volume-title":"Covid-19 screening on chest x-ray images using deep learning based anomaly detection. arXiv preprint arXiv:2003.12338","author":"Jianpeng ZHANG","year":"2020","unstructured":"ZHANG , Jianpeng , XIE , Yutong , LI , Yi , Covid-19 screening on chest x-ray images using deep learning based anomaly detection. arXiv preprint arXiv:2003.12338 , 2020 . ZHANG, Jianpeng, XIE, Yutong, LI, Yi, Covid-19 screening on chest x-ray images using deep learning based anomaly detection. arXiv preprint arXiv:2003.12338, 2020."},{"key":"e_1_3_2_1_17_1","volume-title":"Alexander. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv preprint arXiv:2003.09871","author":"WANG","year":"2020","unstructured":"WANG , Linda et WONG , Alexander. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv preprint arXiv:2003.09871 , 2020 . WANG, Linda et WONG, Alexander. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv preprint arXiv:2003.09871, 2020."},{"key":"e_1_3_2_1_18_1","volume-title":"Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays. arXiv preprint arXiv:2004.08379","author":"Sivaramakrishnan RAJARAMAN","year":"2020","unstructured":"RAJARAMAN , Sivaramakrishnan , SIEGELMAN , Jen , ALDERSON , Philip O. , Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays. arXiv preprint arXiv:2004.08379 , 2020 . RAJARAMAN, Sivaramakrishnan, SIEGELMAN, Jen, ALDERSON, Philip O., Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays. arXiv preprint arXiv:2004.08379, 2020."},{"key":"e_1_3_2_1_19_1","volume-title":"Mohamed Esmail. Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055","author":"Ezz El-Din HEMDAN","year":"2020","unstructured":"HEMDAN , Ezz El-Din , SHOUMAN , Marwa A. , et KARAR , Mohamed Esmail. Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055 , 2020 . HEMDAN, Ezz El-Din, SHOUMAN, Marwa A., et KARAR, Mohamed Esmail. Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055, 2020."},{"key":"e_1_3_2_1_20_1","first-page":"2020","article-title":"Detection of coronavirus disease (covid-19) based on deep features","volume":"2020030300","author":"Prabira SETHY","year":"2020","unstructured":"SETHY , Prabira Kumar et BEHERA, Santi Kumari . Detection of coronavirus disease (covid-19) based on deep features . Preprints , 2020 , vol. 2020030300 , p. 2020 . SETHY, Prabira Kumar et BEHERA, Santi Kumari. Detection of coronavirus disease (covid-19) based on deep features. Preprints, 2020, vol. 2020030300, p. 2020.","journal-title":"Preprints"},{"key":"e_1_3_2_1_21_1","first-page":"1","article-title":"automatic detection from x-ray images utilizing transfer learning with convolutional neural networks","author":"APOSTOLOPOULOS","year":"2020","unstructured":"APOSTOLOPOULOS , Ioannis D. et MPESIANA, Tzani A. Covid-19 : automatic detection from x-ray images utilizing transfer learning with convolutional neural networks . Physical and Engineering Sciences in Medicine , 2020 , p. 1 . APOSTOLOPOULOS, Ioannis D. et MPESIANA, Tzani A. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 2020, p. 1.","journal-title":"Physical and Engineering Sciences in Medicine"},{"key":"e_1_3_2_1_22_1","volume-title":"Multiple-input deep convolutional neural network model for covid-19 forecasting in china. medRxiv","author":"Chiou-Jye HUANG","year":"2020","unstructured":"HUANG , Chiou-Jye , CHEN , Yung-Hsiang , MA , Yuxuan , Multiple-input deep convolutional neural network model for covid-19 forecasting in china. medRxiv , 2020 . HUANG, Chiou-Jye, CHEN, Yung-Hsiang, MA, Yuxuan, Multiple-input deep convolutional neural network model for covid-19 forecasting in china. medRxiv, 2020."},{"key":"e_1_3_2_1_23_1","volume-title":"Shallow Convolutional Neural Network for COVID-19 Outbreak Screening using Chest X-rays","author":"Himadri MUKHERJEE","year":"2020","unstructured":"MUKHERJEE , Himadri , GHOSH , Subhankar , DHAR , Ankita , Shallow Convolutional Neural Network for COVID-19 Outbreak Screening using Chest X-rays . 2020 . MUKHERJEE, Himadri, GHOSH, Subhankar, DHAR, Ankita, Shallow Convolutional Neural Network for COVID-19 Outbreak Screening using Chest X-rays. 2020."},{"key":"e_1_3_2_1_24_1","volume-title":"Su. Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation. medRxiv","author":"Amine AMYAR","year":"2020","unstructured":"AMYAR , Amine , MODZELEWSKI , Romain , et RUAN , Su. Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation. medRxiv , 2020 . AMYAR, Amine, MODZELEWSKI, Romain, et RUAN, Su. Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation. medRxiv, 2020."},{"key":"e_1_3_2_1_25_1","volume-title":"Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. medRxiv","author":"Ying SONG","year":"2020","unstructured":"SONG , Ying , ZHENG , Shuangjia , LI , Liang , Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. medRxiv , 2020 . SONG, Ying, ZHENG, Shuangjia, LI, Liang, Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. medRxiv, 2020."},{"key":"e_1_3_2_1_26_1","volume-title":"MedRxiv","author":"Shuai WANG","year":"2020","unstructured":"WANG , Shuai , KANG , Bo , MA , Jinlu , A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19) . MedRxiv , 2020 . WANG, Shuai, KANG, Bo, MA, Jinlu, A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). MedRxiv, 2020."},{"key":"e_1_3_2_1_27_1","volume-title":"Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv","author":"Chuansheng ZHENG","year":"2020","unstructured":"ZHENG , Chuansheng , DENG , Xianbo , FU , Qing , Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv , 2020 . ZHENG, Chuansheng, DENG, Xianbo, FU, Qing, Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv, 2020."},{"key":"e_1_3_2_1_28_1","first-page":"1","article-title":"Deep learning system to screen coronavirus disease 2019 pneumonia","author":"Charmaine BUTT","year":"2020","unstructured":"BUTT , Charmaine , GILL , Jagpal , CHUN , David , Deep learning system to screen coronavirus disease 2019 pneumonia . Applied Intelligence , 2020 , p. 1 . BUTT, Charmaine, GILL, Jagpal, CHUN, David, Deep learning system to screen coronavirus disease 2019 pneumonia. Applied Intelligence, 2020, p. 1.","journal-title":"Applied Intelligence"},{"key":"e_1_3_2_1_29_1","volume-title":"Saban. Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv preprint arXiv:2003.09424","author":"Mucahid BARSTUGAN","year":"2020","unstructured":"BARSTUGAN , Mucahid , OZKAYA , Umut , et OZTURK , Saban. Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv preprint arXiv:2003.09424 , 2020 . BARSTUGAN, Mucahid, OZKAYA, Umut, et OZTURK, Saban. Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv preprint arXiv:2003.09424, 2020."},{"key":"e_1_3_2_1_30_1","volume-title":"Viral pneumonia. The Lancet","author":"Olli RUUSKANEN","year":"2011","unstructured":"RUUSKANEN , Olli , LAHTI , Elina , JENNINGS , Lance C. , Viral pneumonia. The Lancet , 2011 , vol. 377 , no 9773, p. 1264-1275. RUUSKANEN, Olli, LAHTI, Elina, JENNINGS, Lance C., Viral pneumonia. The Lancet, 2011, vol. 377, no 9773, p. 1264-1275."},{"issue":"10","key":"e_1_3_2_1_31_1","first-page":"1345","article-title":"A survey on transfer learning","volume":"22","author":"Sinno PAN","year":"2009","unstructured":"PAN , Sinno Jialin et YANG, Qiang . A survey on transfer learning . IEEE Transactions on knowledge and data engineering , 2009 , vol. 22 , no 10 , p. 1345 - 1359 . PAN, Sinno Jialin et YANG, Qiang. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 2009, vol. 22, no 10, p. 1345-1359.","journal-title":"IEEE Transactions on knowledge and data engineering"},{"key":"e_1_3_2_1_32_1","first-page":"1251","article-title":"Deep learning with depthwise separable convolutions. In","author":"Fran\u00e7ois CHOLLET","year":"2017","unstructured":"CHOLLET , Fran\u00e7ois . Xception : Deep learning with depthwise separable convolutions. In : Proceedings of the IEEE conference on computer vision and pattern recognition. 2017 . p. 1251 - 1258 . CHOLLET, Fran\u00e7ois. Xception: Deep learning with depthwise separable convolutions. In : Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. p. 1251-1258.","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition."},{"key":"e_1_3_2_1_33_1","volume-title":"Andrew. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"SIMONYAN","year":"2014","unstructured":"SIMONYAN , Karen et ZISSERMAN , Andrew. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 , 2014 . SIMONYAN, Karen et ZISSERMAN, Andrew. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014."},{"key":"e_1_3_2_1_34_1","first-page":"770","article-title":"Deep residual learning for image recognition. In","author":"Kaiming HE","year":"2016","unstructured":"HE , Kaiming , ZHANG , Xiangyu , REN , Shaoqing , Deep residual learning for image recognition. In : Proceedings of the IEEE conference on computer vision and pattern recognition. 2016 . p. 770 - 778 . HE, Kaiming, ZHANG, Xiangyu, REN, Shaoqing, Deep residual learning for image recognition. In : Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 770-778.","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition."},{"key":"e_1_3_2_1_35_1","volume-title":"Shuicheng. Network in network. arXiv preprint arXiv:1312.4400","author":"Min LIN","year":"2013","unstructured":"LIN , Min , CHEN , Qiang , et YAN , Shuicheng. Network in network. arXiv preprint arXiv:1312.4400 , 2013 . LIN, Min, CHEN, Qiang, et YAN, Shuicheng. Network in network. arXiv preprint arXiv:1312.4400, 2013."},{"key":"e_1_3_2_1_36_1","first-page":"1","article-title":"automatic detection from x-ray images utilizing transfer learning with convolutional neural networks","author":"APOSTOLOPOULOS","year":"2020","unstructured":"APOSTOLOPOULOS , Ioannis D. et MPESIANA, Tzani A. Covid-19 : automatic detection from x-ray images utilizing transfer learning with convolutional neural networks . Physical and Engineering Sciences in Medicine , 2020 , p. 1 . APOSTOLOPOULOS, Ioannis D. et MPESIANA, Tzani A. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 2020, p. 1.","journal-title":"Physical and Engineering Sciences in Medicine"},{"key":"e_1_3_2_1_37_1","volume-title":"Alexander. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv preprint arXiv:2003.09871","author":"WANG","year":"2020","unstructured":"WANG , Linda et WONG , Alexander. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv preprint arXiv:2003.09871 , 2020 . WANG, Linda et WONG, Alexander. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. arXiv preprint arXiv:2003.09871, 2020."},{"key":"e_1_3_2_1_38_1","volume-title":"Mohamed Medhat. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. arXiv preprint arXiv:2003.13815","author":"Asmaa ABBAS","year":"2020","unstructured":"ABBAS , Asmaa , ABDELSAMEA , Mohammed M. , et GABER , Mohamed Medhat. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. arXiv preprint arXiv:2003.13815 , 2020 ABBAS, Asmaa, ABDELSAMEA, Mohammed M., et GABER, Mohamed Medhat. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. arXiv preprint arXiv:2003.13815, 2020"},{"key":"e_1_3_2_1_39_1","volume-title":"Dongxiao. Covid-xpert: An ai powered population screening of covid-19 cases using chest radiography images. arXiv preprint arXiv:2004.03042","author":"LI","year":"2020","unstructured":"LI , Xin et ZHU , Dongxiao. Covid-xpert: An ai powered population screening of covid-19 cases using chest radiography images. arXiv preprint arXiv:2004.03042 , 2020 . LI, Xin et ZHU, Dongxiao. Covid-xpert: An ai powered population screening of covid-19 cases using chest radiography images. arXiv preprint arXiv:2004.03042, 2020."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","article-title":"Automated detection of COVID-19 cases using deep neural networks with X-ray images","author":"Tulin OZTURK","year":"2020","unstructured":"OZTURK , Tulin , TALO , Muhammed , YILDIRIM , Eylul Azra , Automated detection of COVID-19 cases using deep neural networks with X-ray images . Computers in Biology and Medicine , 2020 , p. 103792 . OZTURK, Tulin, TALO, Muhammed, YILDIRIM, Eylul Azra, Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, 2020, p. 103792.","journal-title":"Computers in Biology and Medicine"},{"key":"e_1_3_2_1_41_1","first-page":"1","article-title":"automatic detection from x-ray images utilizing transfer learning with convolutional neural networks","author":"APOSTOLOPOULOS","year":"2020","unstructured":"APOSTOLOPOULOS , Ioannis D. et MPESIANA, Tzani A. Covid-19 : automatic detection from x-ray images utilizing transfer learning with convolutional neural networks . Physical and Engineering Sciences in Medicine , 2020 , p. 1 . APOSTOLOPOULOS, Ioannis D. et MPESIANA, Tzani A. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 2020, p. 1.","journal-title":"Physical and Engineering Sciences in Medicine"}],"event":{"name":"NISS2021: The 4th International Conference on Networking, Information Systems & Security.","acronym":"NISS2021","location":"KENITRA AA Morocco"},"container-title":["Proceedings of the 4th International Conference on Networking, Information Systems & Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3454127.3457615","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3454127.3457615","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T17:49:52Z","timestamp":1750268992000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3454127.3457615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4]]},"references-count":41,"alternative-id":["10.1145\/3454127.3457615","10.1145\/3454127"],"URL":"https:\/\/doi.org\/10.1145\/3454127.3457615","relation":{},"subject":[],"published":{"date-parts":[[2021,4]]},"assertion":[{"value":"2021-11-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}