{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:59:03Z","timestamp":1743101943941,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030821982"},{"type":"electronic","value":"9783030821999"}],"license":[{"start":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T00:00:00Z","timestamp":1628294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T00:00:00Z","timestamp":1628294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-82199-9_33","type":"book-chapter","created":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T09:02:45Z","timestamp":1636534965000},"page":"502-516","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Analysis of Machine Learning Algorithms Using COVID-19 Chest X-ray Images and Dataset"],"prefix":"10.1007","author":[{"given":"Abraham","family":"Kumah","sequence":"first","affiliation":[]},{"given":"Osama","family":"Abuomar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,7]]},"reference":[{"key":"33_CR1","unstructured":"Covid-19.who.int. 2020. WHO Coronavirus Disease (COVID-19) Dashboard. https:\/\/covid19.who.int\/. Accessed 8 Nov 2020"},{"key":"33_CR2","unstructured":"Centers for Disease Control and Preventon 2020. Coronavirus Disease 2019 (COVID-19) \u2013 Symptoms. https:\/\/www.cdc.gov\/coronavirus\/2019-ncov\/symptoms-testing\/symptoms.html, Accessed 8 Nov 2020"},{"key":"33_CR3","unstructured":"Center for Disease Control and Prevention. 2020. COVID-19 Pandemic Planning Scenarios. https:\/\/www.cdc.gov\/coronavirus\/2019-ncov\/hcp\/planning-scenarios.html, Accessed 13 Nov 2020"},{"key":"33_CR4","unstructured":"McCrimmon, K.: The Truth About Asymptomatic Spread Of COVID-19. Uchealth Today. UCHealth Today (2020). https:\/\/www.uchealth.org\/today\/the-truth-about-asymptomatic-spread-of-covid-19\/. Accessed 13 Nov 2020"},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Smith, D., Grenier, J., Batte, C., Spieler, B.: A Characteristic Chest Radiographic Pattern In The Setting Of COVID-19 Pandemic. Radiology: Cardiothoracic Imaging (2020). Pubs.rsna.org. https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020200280. Accessed 12 Nov 2020","DOI":"10.1148\/ryct.2020200280"},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Ozturk, T., Talo, M., Yildirim, E., Baloglu, U., Yildirim, O., Acharya, U.: Automated Detection of COVID-19 Cases Using Deep Neural Networks With X-Ray Images. Computers in Biology and Medicine, vol. 121, 103792, June 2020. Automated detection of COVID-19 cases using deep neural networks with X-ray images \u2013 ScienceDirect. Accessed 12 Nov 2020","DOI":"10.1016\/j.compbiomed.2020.103792"},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Cohen, J.P., et al.: Predicting COVID-19 pneumonia severity on chest X-ray with deep learning. https:\/\/arxiv.org\/pdf\/2005.11856.pdf. Accessed 12 Nov 2020","DOI":"10.7759\/cureus.9448"},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Asif, S., Wenhui, Y., Jin, H., Tao, Y., Jinhai, S.: 2020. Classification of Covid-19 from Chest X-ray Images Using Deep Convolutional Neural Networks. https:\/\/www.medrxiv.org\/content\/10.1101\/2020.05.01.20088211v2. Accessed 1 Nov 2020","DOI":"10.1109\/ICCC51575.2020.9344870"},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Apostolopoulos, L., Mpesiana, T.: 2020 Covid-19: Automatic Detection from X-ray Images UtilizingTransfer Learning with Convolutional Neural Networks. https:\/\/link.springer.a3wzescom\/article\/10.1007\/s13246-020-00865-4. Accessed 2 Nov 2020","DOI":"10.1007\/s13246-020-00865-4"},{"key":"33_CR10","unstructured":"Dutta, H.: Neural Network Model for Prediction of Covid-19 Confirmed Cases and Fatalities, Researchgate May, 2020. https:\/\/www.researchgate.net\/publication\/341089678_Neural_Network_Model_for_Prediction_of_Covid-19_Confirmed_Cases_and_Fatalities. Accessed 2 Nov 2020"},{"key":"33_CR11","unstructured":"Majeed, T., Rashid, R., Ali, D., Asad, A.: 2020. Covid-19 detection using CNN transfer learning from X-ray Images. Medrxiv.org. https:\/\/www.medrxiv.org\/content\/10.1101\/2020.05.12.20098954v2.full.pdf. Accessed 2 Nov 2020"},{"key":"33_CR12","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.matcom.2020.04.031","volume":"177","author":"MV Valueva","year":"2020","unstructured":"Valueva, M.V., Nagornov, N.N., Lyakhov, P.A., Valuev, G.V., Chervyakov, N.I.: Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Math. Comput. Simul. Elsevier BV. 177, 232\u2013243 (2020)","journal-title":"Math. Comput. Simul. Elsevier BV."},{"key":"33_CR13","unstructured":"Brownlee, J.: 2020. How To Use The Pre-Trained VGG Model To Classify Objects In Photographs. Machine Learning Mastery. www.machinelearningmastery.com\/use-pre-trained-vgg-model-classify-objects-photographs\/. Accesssed 2 Nov 2020"},{"key":"33_CR14","unstructured":"Hassan, M.: VGG16 - Convolutional Network for Classification and Detection. Neurohive.io. https:\/\/neurohive.io\/en\/popular-networks\/vgg16\/. Accessed 2 Nov 2020"},{"key":"33_CR15","unstructured":"Thakur, R.: Step By Step VGG16 Implementation In Keras For Beginners. Medium. https:\/\/towardsdatascience.com\/step-by-step-vgg16-implementation-in-keras-for-beginners-a833c686ae6c. Accessed 2 Nov 2020"},{"key":"33_CR16","unstructured":"Hassan, M.: VGG16 - Convolutional Network for Classification and Detection. Neurohive.io. https:\/\/neurohive.io\/en\/popular-networks\/vgg16\/, Accessed 2 Nov 2020"},{"issue":"01","key":"33_CR17","first-page":"1","volume":"05","author":"Q Qin","year":"2013","unstructured":"Qin, Q., Wang, Q., Li, J., Ge, S.: Linear and nonlinear trading models with gradient boosted random forests and applications to Singapore stock market. J. Intell. Learn. Syst. Appl. 05(01), 1\u201310 (2013)","journal-title":"J. Intell. Learn. Syst. Appl."},{"key":"33_CR18","unstructured":"Liberman, N.: Decisoni Trees and Random Forests. https:\/\/towardsdatascience.com\/decision-trees-and-random-forests-df0c3123. Accessed 18 Feb 2021"},{"key":"33_CR19","unstructured":"Ho, T.K.: Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, pp. 278\u2013282, 14\u201316 August 1995"},{"key":"33_CR20","first-page":"1","volume":"5","author":"Q Qin","year":"2013","unstructured":"Qin, Q., Wang, Q.-G., Li, J., Ge, S.S.: Linear and nonlinear trading models with gradient boosted Rrandom forests and applications to Singapore stock market. J. Intell. Learn. Syst. Appl. 5, 1\u201310 (2013)","journal-title":"J. Intell. Learn. Syst. Appl."},{"key":"33_CR21","unstructured":"Donges, N.: A Complete Guide to the Random Forest Algorithm. https:\/\/builtin.com\/data-science\/random-forest-algorithm. Accessed 24 Feb 2021"},{"key":"33_CR22","unstructured":"Brownlee, J.: A Gentle Introduction To XGBoost For Applied Machine Learning. Machine Learning Mastery. https:\/\/machinelearningmastery.com\/gentle-introduction-xgboost-applied-machine-learning\/. Accessed 2 Nov 2020"},{"key":"33_CR23","unstructured":"Ahmed, W.: The Curse of Color Order Conflicts On Deep Learning. https:\/\/medium.com\/@walmaly\/the-curse-of-color-order-conflicts-on-deep-learning-1e58b765d40e. Accessed 2 Nov 2020"},{"key":"33_CR24","unstructured":"Chen, T., Guestrin, C., XGBoost: A Scalable Tree Boosting System. In Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 13\u201317 August 2016. https:\/\/arxiv.org\/pdf\/1603.02754.pdf, Accessed 24 Feb 2021"},{"key":"33_CR25","unstructured":"Gupta, S.: Pros and Cons of Various Machine Learning Algorithms. https:\/\/towardsdatascience.com\/pros-and-cons-of-various-classification-ml-algorithms-3b5bfb3c87d6. Accessed 24 Feb 2021"},{"key":"33_CR26","unstructured":"Machine Learning Algorithms: strengths and weaknesses. https:\/\/elitedatascience.com\/machine-learning-algorithms. Accessed 24 Feb 2021"},{"key":"33_CR27","unstructured":"Github: 2020. Ieee8023\/Covid-Chestxray-Dataset. https:\/\/github.com\/ieee8023\/covid-chestxray-dataset. Accessed 1 Nov 2020"},{"key":"33_CR28","unstructured":"Kaggle: COVID-19 Radiography Database. https:\/\/www.kaggle.com\/tawsifurrahman\/covid19-radiography-database. Accessed 2 Nov 2020"},{"key":"33_CR29","unstructured":"Kaggle: COVID-19 Chest X-Ray Image dataset. https:\/\/www.kaggle.com\/alifrahman\/covid19-chest-xray-image-dataset. Accessed 2 Nov 2020"},{"key":"33_CR30","unstructured":"Narkhede, S.: Understanding Confusion Matrix. Medium. https:\/\/towardsdatascience.com\/understanding-confusion-matrix-a9ad42dcfd62. Accessed 2 Nov 2020"},{"key":"33_CR31","unstructured":"Keras.io.2020. Keras Documentation: Image Data Preprocessing. https:\/\/keras.io\/api\/preprocessing\/image\/. Accessed 30 Oct 2020"},{"key":"33_CR32","unstructured":"McCrimmon, K.: The Truth About Asymptomatic Spread of COVID-19. Uchealth Today. UCHealth Today. https:\/\/www.uchealth.org\/today\/the-truth-about-asymptomatic-spread-of-covid-19\/. Accessed 2 Nov 2020"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-82199-9_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T09:10:09Z","timestamp":1636535409000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-82199-9_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,7]]},"ISBN":["9783030821982","9783030821999"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-82199-9_33","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2021,8,7]]},"assertion":[{"value":"7 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IntelliSys","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Proceedings of SAI Intelligent Systems Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"intellisys2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/saiconference.com\/IntelliSys","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}