ijmsit International Journal of Multidisciplinary Studies and Innovative Technologies 2602-4888 2602-4888 SET Teknoloji Deep Learning Derin Öğrenme COVID-19 and Non-COVID-19 Classification from Lung CT-Scan Images Using Deep Convolutional Neural Networks https://orcid.org/0000-0002-3947-898X Dolma Özlü PAMUKKALE ÜNİVERSİTESİ 12 19 2023 7 2 53 60 10 27 2023 12 05 2023 Copyright © 2017, International Journal of Multidisciplinary Studies and Innovative Technologies 2017 International Journal of Multidisciplinary Studies and Innovative Technologies

In this study, three different convolutional neural network (CNN) architectures have been used for SARS-COV-2 infection (COVID-19) detection from lung Computerized Tomography (CT) scan images. The dataset comprises 2481 lung CT-scan images, of which 1252 are positive for COVID-19 infection. First, a simple CNN, LeNet-5, was trained from scratch, which resulted in poor classification performance with an accuracy value of 0.78. Then, to overcome the drawback of the limited availability of data, the convolutional bases of two pre-trained networks, VGG-16 and MobileNet, were leveraged to extract features from the dataset. On top of the feature extraction outputs, new classifiers were trained. When the VGG16 and the MobileNet CNN’s convolutional bases were used for feature extraction, accuracy values of 0.974 and 0.984 were obtained, respectively. The findings indicate that using pre-trained CNN models for feature extraction and then training a simpler, fully connected network structure for classification successfully differentiates CT-scan images of patients with COVID-19 infection from the ones without COVID-19 infection.

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