In the two classification tasks (binary and three-class), it was observed that the fine-tuned VGG-16 DTL model had stronger positive correlations in the MCC metric than the fine-tuned VGG-19 DTL model. These results showed strong positive correlations between the models’ predictions and the true labels. Moreover, the fine-tuned VGG-16 and VGG-19 models have MCC of 0.98 and 0.96 respectively in the binary classification, and 0.91 and 0.89 for multiclass classification. In contrast, in the multiclass (three-class) task, the fine-tuned VGG-16 and VGG-19 DTL models produced an accuracy of 93.85% and 92.92%, respectively. The results showed that the fine-tuned VGG-16 and VGG-19 models produced an accuracy of 99.23% and 98.00%, respectively, in the binary task. The fine-tuned VGG-16 and VGG-19 DTL were modelled by employing a batch size of 10 in 40 epochs, Adam optimizer for weight updates, and categorical cross-entropy loss function. The system was trained with an X-ray image dataset for the detection of COVID-19. Four experiments were performed where fine-tuned VGG-16 and VGG-19 Convolutional Neural Networks (CNNs) with DTL were trained on both binary and three-class datasets that contain X-ray images. This paper used Deep Transfer Learning Model (DTL) for the classification of a real-life COVID-19 dataset of chest X-ray images in both binary (COVID-19 or Normal) and three-class (COVID-19, Viral-Pneumonia or Normal) classification scenarios. Research showed that relentless efforts had been made to improve key performance indicators for detection, isolation, and early treatment. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Coronavirus-2 or SARS-CoV-2), which came into existence in 2019, is a viral pandemic that caused coronavirus disease 2019 (COVID-19) illnesses and death. Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset. Cite this article Fayemiwo MA, Olowookere TA, Arekete SA, Ogunde AO, Odim MO, Oguntunde BO, Olaniyan OO, Ojewumi TO, Oyetade IS, Aremu AA, Kayode AA. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. 2 Radiology Department, Ladoke Akintola University of Technology, Ogbomoso, Oyo, Nigeria DOI 10.7717/peerj-cs.614 Published Accepted Received Academic Editor Davide Chicco Subject Areas Bioinformatics, Artificial Intelligence, Computer Vision, Data Mining and Machine Learning Keywords Convolutional neural networks, Coronavirus, COVID-19 test results, Deep transfer learning, Machine learning, VGG-16, VGG-19 Copyright © 2021 Fayemiwo et al.
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