Show simple item record

dc.contributor.authorVarshney, Ashutosh
dc.contributor.authorSubasi, Abdulhamit
dc.date.accessioned2023-03-13T10:48:17Z
dc.date.available2023-03-13T10:48:17Z
dc.date.issued2023-01-20
dc.identifier.citationAshutosh Varshney, Abdulhamit Subasi, Chapter 8 - A deep learning approach for COVID-19 detection from computed tomography scans, Editor(s): Abdulhamit Subasi, In Artificial Intelligence Applications in Healthcare&Medicine, Applications of Artificial Intelligence in Medical Imaging, Academic Press, 2023, Pages 223-240, ISBN 9780443184505, https://doi.org/10.1016/B978-0-443-18450-5.00011-6. (https://www.sciencedirect.com/science/article/pii/B9780443184505000116)en_US
dc.identifier.isbn9780443184505en_US
dc.identifier.doihttps://doi.org/10.1016/B978-0-443-18450-5.00011-6en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/633
dc.description.abstractThe classification of COVID-19 patients from chest computed tomography (CT) images is a very difficult task due to the similarities observed with other lung diseases. Based on various CT scans of COVID and non-COVID patients, the aim of this chapter is to propose a simple deep learning architecture and compare its diagnostic performance using transfer learning and several machine learning techniques that could extract COVID-19’s graphical features and classify them in order to provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. We also compare our approach and show that it outperforms various previous state-of-the-art techniques. We propose a deep learning architecture for transfer learning that is just a simple modification of eight new layers on the ImageNet pretrained convolutional neural networks (CNNs) which yielded us the best test accuracy of 98.30%, F1 score of 0.982, area under the receiver operating characteristic (ROC) curve of 0.982, and kappa value of 0.964 after training. Moreover, we use the proposed architecture for feature extraction and study the performance of various classifiers on them and were able to obtain the highest test accuracy of 91.75% with K-nearest neighbors. Also, we compare multiple CNNs and machine learning models for their diagnostic potential in disease detection and suggest a much faster and automated disease detection methodology. We show that smaller and memory efficient architectures are equally good compared to deep and heavy architectures at classifying chest CTs. We also show that visual geometry group (VGG) architectures are overall the best for this task.en_US
dc.publisherAcademic Pressen_US
dc.subjectCOVID-19en_US
dc.subjectDeep Learningen_US
dc.subjectTransfer Learningen_US
dc.subjectConvolutional Neural Networks (CNN)en_US
dc.titleA deep learning approach for COVID-19 detection from computed tomography scansen_US
dc.source.booktitleApplications of Artificial Intelligence in Medical Imaging.en_US
dc.source.volumeArtificial Intelligence Applications in Healthcare&Medicine;
dc.source.pages223-240en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAHEALTHen_US
dc.source.indexScopusen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorAshutosh, Varshney


This item appears in the following Collection(s)

Show simple item record