{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:19:00Z","timestamp":1750220340392,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"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":[[2021,12,15]]},"DOI":"10.1145\/3508072.3508094","type":"proceedings-article","created":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T17:01:56Z","timestamp":1649869316000},"page":"128-137","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Survey on Deep Learning and Machine Learning for COVID-19 Detection"],"prefix":"10.1145","author":[{"given":"Mohamed","family":"M. Dessouky","sequence":"first","affiliation":[{"name":"University of Jeddah, Saudi Arabia"}]},{"given":"Sahar F.","family":"Sabbeh","sequence":"additional","affiliation":[{"name":"University of Jeddah, Saudi Arabia"}]},{"given":"Boushra","family":"Alshehri","sequence":"additional","affiliation":[{"name":"University of Jeddah, Saudi Arabia"}]}],"member":"320","published-online":{"date-parts":[[2022,4,13]]},"reference":[{"volume-title":"COVID Live Update: 246,945,654 Cases and 5,007,977 Deaths from the Coronavirus - Worldometer","year":"2021","key":"e_1_3_2_1_1_1","unstructured":"\u201c COVID Live Update: 246,945,654 Cases and 5,007,977 Deaths from the Coronavirus - Worldometer .\u201d https:\/\/www.worldometers.info\/coronavirus\/ (accessed Oct. 30, 2021 ). \u201cCOVID Live Update: 246,945,654 Cases and 5,007,977 Deaths from the Coronavirus - Worldometer.\u201d https:\/\/www.worldometers.info\/coronavirus\/ (accessed Oct. 30, 2021)."},{"volume-title":"SARS-CoV-2 Variant Classifications and Definitions","year":"2019","key":"e_1_3_2_1_2_1","unstructured":"\u201c SARS-CoV-2 Variant Classifications and Definitions .\u201d https:\/\/www.cdc.gov\/coronavirus\/ 2019 -ncov\/variants\/variant-info.html (accessed Oct. 30, 2021). \u201cSARS-CoV-2 Variant Classifications and Definitions.\u201d https:\/\/www.cdc.gov\/coronavirus\/2019-ncov\/variants\/variant-info.html (accessed Oct. 30, 2021)."},{"volume-title":"How Many Coronavirus Strains are There? Novel Coronavirus Types and More","year":"2021","key":"e_1_3_2_1_3_1","unstructured":"\u201c How Many Coronavirus Strains are There? Novel Coronavirus Types and More .\u201d https:\/\/www.webmd.com\/lung\/coronavirus-strains#1 (accessed Oct. 30, 2021 ). \u201cHow Many Coronavirus Strains are There? Novel Coronavirus Types and More.\u201d https:\/\/www.webmd.com\/lung\/coronavirus-strains#1 (accessed Oct. 30, 2021)."},{"key":"#cr-split#-e_1_3_2_1_4_1.1","doi-asserted-by":"crossref","unstructured":"Serener. A and S. Serte 2010 \"Deep learning to distinguish COVID-19 from other lung infections pleural diseases and lung tumors \" Nov. 2020. doi: 10.1109\/TIPTEKNO50054.2020.9299215. 10.1109\/TIPTEKNO50054.2020.9299215","DOI":"10.1109\/TIPTEKNO50054.2020.9299215"},{"key":"#cr-split#-e_1_3_2_1_4_1.2","doi-asserted-by":"crossref","unstructured":"Serener. A and S. Serte 2010 \"Deep learning to distinguish COVID-19 from other lung infections pleural diseases and lung tumors \" Nov. 2020. doi: 10.1109\/TIPTEKNO50054.2020.9299215.","DOI":"10.1109\/TIPTEKNO50054.2020.9299215"},{"key":"e_1_3_2_1_5_1","volume-title":"BioMedical Engineering Online","volume":"19","author":"Hussain L.","unstructured":"Hussain , L. , Nguyen , T. , Li , H. , Abbasi , A.A. , Lone , K.J. , Zhao , Z. , Zaib , M. , Chen , A. and Duong , T.Q. , 2020 \u201cMachine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection ,\u201d BioMedical Engineering Online , vol. 19 , no. 1, Dec. 2020, doi: 10.1186\/s12938-020-00831-x. 10.1186\/s12938-020-00831-x Hussain, L., Nguyen, T., Li, H., Abbasi, A.A., Lone, K.J., Zhao, Z., Zaib, M., Chen, A. and Duong, T.Q., 2020 \u201cMachine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection,\u201d BioMedical Engineering Online, vol. 19, no. 1, Dec. 2020, doi: 10.1186\/s12938-020-00831-x."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Nguyen T.T. Nguyen Q.V.H. Nguyen D.T. Hsu E.B. Yang S. and Eklund P. 2020 \u201cArtificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions \u201d Jul. 2020 [Online]. Available: http:\/\/arxiv.org\/abs\/2008.07343  Nguyen T.T. Nguyen Q.V.H. Nguyen D.T. Hsu E.B. Yang S. and Eklund P. 2020 \u201cArtificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions \u201d Jul. 2020 [Online]. Available: http:\/\/arxiv.org\/abs\/2008.07343","DOI":"10.36227\/techrxiv.12743933"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18031117"},{"key":"e_1_3_2_1_8_1","volume-title":"Information (Switzerland)","volume":"11","author":"Khan I.U.","unstructured":"Khan , I.U. and Aslam , N. , 2020., \u201cA deep-learning-based framework for automated diagnosis of COVID-19 using X-ray images ,\u201d Information (Switzerland) , vol. 11 , no. 9, 2020, doi: 10.3390\/INFO11090419. 10.3390\/INFO11090419 Khan, I.U. and Aslam, N., 2020., \u201cA deep-learning-based framework for automated diagnosis of COVID-19 using X-ray images,\u201d Information (Switzerland), vol. 11, no. 9, 2020, doi: 10.3390\/INFO11090419."},{"key":"e_1_3_2_1_9_1","unstructured":"Eldeen N. and M. Khalifa 2020. \u201cDetection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset.\u201d [Online]. Available: http:\/\/www.egyptscience.net  Eldeen N. and M. Khalifa 2020. \u201cDetection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset.\u201d [Online]. Available: http:\/\/www.egyptscience.net"},{"key":"e_1_3_2_1_10_1","volume-title":"Sensors","volume":"21","author":"Khasawneh N.","unstructured":"Khasawneh , N. , Fraiwan , M. , Fraiwan , L. , Khassawneh , B. and Ibnian , A. , 2021, \u201cDetection of covid-19 from chest x-ray images using deep convolutional neural networks ,\u201d Sensors , vol. 21 , no. 17, Sep. 2021, doi: 10.3390\/s21175940. 10.3390\/s21175940 Khasawneh, N., Fraiwan, M., Fraiwan, L., Khassawneh, B. and Ibnian, A., 2021, \u201cDetection of covid-19 from chest x-ray images using deep convolutional neural networks,\u201d Sensors, vol. 21, no. 17, Sep. 2021, doi: 10.3390\/s21175940."},{"key":"e_1_3_2_1_11_1","volume-title":"Applied Sciences (Switzerland)","volume":"10","author":"Duran-Lopez L.","unstructured":"Duran-Lopez , L. , Dominguez-Morales , J.P. , Corral-Jaime , J. , Vicente-Diaz , S. and Linares-Barranco , A. , 2020., \u201cCOVID-XNet: A custom deep learning system to diagnose and locate COVID-19 in chest x-ray images ,\u201d Applied Sciences (Switzerland) , vol. 10 , no. 16, Aug. 2020, doi: 10.3390\/app10165683. 10.3390\/app10165683 Duran-Lopez, L., Dominguez-Morales, J.P., Corral-Jaime, J., Vicente-Diaz, S. and Linares-Barranco, A., 2020., \u201cCOVID-XNet: A custom deep learning system to diagnose and locate COVID-19 in chest x-ray images,\u201d Applied Sciences (Switzerland), vol. 10, no. 16, Aug. 2020, doi: 10.3390\/app10165683."},{"key":"e_1_3_2_1_12_1","first-page":"178","volume-title":"Proceedings, IEEE 3rd International Conference and Workshop in Obuda on Electrical and Power Engineering","author":"Tabrizchi H.","year":"2020","unstructured":"Tabrizchi , H. , Mosavi , A. , Szabo-Gali , A. , Felde , I. and Nadai , L. , 2020, \u201cRapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans,\u201d in CANDO-EPE 2020 - Proceedings, IEEE 3rd International Conference and Workshop in Obuda on Electrical and Power Engineering , Nov. 2020 , pp. 173\u2013 178 . doi: 10.1109\/CANDO-EPE51100.2020.9337794. 10.1109\/CANDO-EPE51100.2020.9337794 Tabrizchi, H., Mosavi, A., Szabo-Gali, A., Felde, I. and Nadai, L., 2020, \u201cRapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans,\u201d in CANDO-EPE 2020 - Proceedings, IEEE 3rd International Conference and Workshop in Obuda on Electrical and Power Engineering, Nov. 2020, pp. 173\u2013178. doi: 10.1109\/CANDO-EPE51100.2020.9337794."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPET51420.2020.00009"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2021.05.013"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12539-020-00403-6"},{"key":"e_1_3_2_1_16_1","volume-title":"Soft Computing","author":"Al-Waisy A.S.","year":"2020","unstructured":"Al-Waisy , A.S. , Al-Fahdawi , S. , Mohammed , M.A. , Abdulkareem , K.H. , Mostafa , S.A. , Maashi , M.S. , Arif , M. and Garcia-Zapirain , B. , 2020. \u201cCOVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images ,\u201d Soft Computing , 2020 , doi: 10.1007\/s00500-020-05424-3. 10.1007\/s00500-020-05424-3 Al-Waisy, A.S., Al-Fahdawi, S., Mohammed, M.A., Abdulkareem, K.H., Mostafa, S.A., Maashi, M.S., Arif, M. and Garcia-Zapirain, B., 2020. \u201cCOVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images,\u201d Soft Computing, 2020, doi: 10.1007\/s00500-020-05424-3."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2020.04.010"},{"key":"e_1_3_2_1_18_1","first-page":"1","volume-title":"Physical and Engineering Sciences in Medicine","author":"Arora R.","unstructured":"Arora , R. , Bansal , V. , Buckchash , H. , Kumar , R. , Sahayasheela , V.J. , Narayanan , N. , Pandian , G.N. and Raman , B ., 2021. AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs . Physical and Engineering Sciences in Medicine , pp. 1 - 15 . Arora, R., Bansal, V., Buckchash, H., Kumar, R., Sahayasheela, V.J., Narayanan, N., Pandian, G.N. and Raman, B., 2021. AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs. Physical and Engineering Sciences in Medicine, pp.1-15."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200905"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010287"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCC51575.2020.9344870"},{"key":"e_1_3_2_1_23_1","volume-title":"Applied Soft Computing","volume":"98","author":"Aslan M.F.","year":"2021","unstructured":"Aslan , M.F. , Unlersen , M.F. , Sabanci , K. and Durdu , A. , 2021. \u201cCNN-based transfer learning\u2013BiLSTM network: A novel approach for COVID-19 infection detection ,\u201d Applied Soft Computing , vol. 98 , Jan. 2021 , doi: 10.1016\/j.asoc.2020.106912. 10.1016\/j.asoc.2020.106912 Aslan, M.F., Unlersen, M.F., Sabanci, K. and Durdu, A., 2021. \u201cCNN-based transfer learning\u2013BiLSTM network: A novel approach for COVID-19 infection detection,\u201d Applied Soft Computing, vol. 98, Jan. 2021, doi: 10.1016\/j.asoc.2020.106912."},{"key":"e_1_3_2_1_24_1","volume-title":"Computers in Biology and Medicine","volume":"121","author":"To\u011fa\u00e7ar M.","year":"2020","unstructured":"To\u011fa\u00e7ar , M. , Ergen , B. and C\u00f6mert , Z. , 2020, \u201cCOVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches ,\u201d Computers in Biology and Medicine , vol. 121 , Jun. 2020 , doi: 10.1016\/j.compbiomed.2020.103805. 10.1016\/j.compbiomed.2020.103805 To\u011fa\u00e7ar, M., Ergen, B. and C\u00f6mert, Z., 2020, \u201cCOVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches,\u201d Computers in Biology and Medicine, vol. 121, Jun. 2020, doi: 10.1016\/j.compbiomed.2020.103805."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110245"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCMC51019.2021.9418398"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07044-9"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3057683"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200905"},{"key":"e_1_3_2_1_30_1","volume-title":"Image and Video Processing","author":"Kamal K.C.","year":"2021","unstructured":"Kamal , K.C. , Yin , Z. , Wu , M. and Wu , Z. , 2021. \u201cEvaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images,\u201d Signal , Image and Video Processing , 2021 , doi: 10.1007\/s11760-020-01820-2. 10.1007\/s11760-020-01820-2 Kamal, K.C., Yin, Z., Wu, M. and Wu, Z., 2021. \u201cEvaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images,\u201d Signal, Image and Video Processing, 2021, doi: 10.1007\/s11760-020-01820-2."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-021-00984-y"},{"key":"e_1_3_2_1_32_1","volume-title":"Applied Intelligence","volume":"51","author":"Jain R.","unstructured":"Jain , R. , Gupta , M. , Taneja , S. and Hemanth , D.J. , 202. \u201cDeep learning based detection and analysis of COVID-19 on chest X-ray images ,\u201d Applied Intelligence , vol. 51 , no. 3, pp. 1690\u20131700, Mar. 2021, doi: 10.1007\/s10489-020-01902-1. 10.1007\/s10489-020-01902-1 Jain, R., Gupta, M., Taneja, S. and Hemanth, D.J., 202. \u201cDeep learning based detection and analysis of COVID-19 on chest X-ray images,\u201d Applied Intelligence, vol. 51, no. 3, pp. 1690\u20131700, Mar. 2021, doi: 10.1007\/s10489-020-01902-1."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113909"},{"key":"e_1_3_2_1_34_1","volume-title":"Applied Soft Computing","volume":"97","author":"Nour M.","year":"2020","unstructured":"Nour , M. , C\u00f6mert , Z. and Polat , K. , 2020. \u201cA Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization ,\u201d Applied Soft Computing , vol. 97 , Dec. 2020 , doi: 10.1016\/j.asoc.2020.106580 10.1016\/j.asoc.2020.106580 Nour, M., C\u00f6mert, Z. and Polat, K., 2020. \u201cA Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization,\u201d Applied Soft Computing, vol. 97, Dec. 2020, doi: 10.1016\/j.asoc.2020.106580"}],"event":{"name":"ICFNDS 2021: The 5th International Conference on Future Networks & Distributed Systems","acronym":"ICFNDS 2021","location":"Dubai United Arab Emirates"},"container-title":["The 5th International Conference on Future Networks & Distributed Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3508072.3508094","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3508072.3508094","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:30Z","timestamp":1750191150000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3508072.3508094"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,15]]},"references-count":35,"alternative-id":["10.1145\/3508072.3508094","10.1145\/3508072"],"URL":"https:\/\/doi.org\/10.1145\/3508072.3508094","relation":{},"subject":[],"published":{"date-parts":[[2021,12,15]]},"assertion":[{"value":"2022-04-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}