International Journal of Web Research

International Journal of Web Research

A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan Images

Document Type : Original Article

Authors
touba torabipour 1
abolfazl gandomi 1
mohammad ghanimi 2
1 Department of Computer, Yazd Branch, Islamic Azad University, Yazd, Iran;
2 Department of Computer, Ershad Damavand University, Tehran, Iran
10.22133/ijwr.2024.449726.1211
Abstract
Lung infection represents one of the most perilous indicators of Covid-19. The most efficient diagnostic approach entails the analysis of CT scan images. Utilizing deep learning algorithms and machine vision, computer scientists have devised a method for automated detection of this disease. This study proposes a two-stage approach to identifying lung infection. In the initial stage, image features are extracted through a transfer learning framework employing ResNet50, with the last two layers being fixed. Subsequently, a CNN neural network is constructed for image detection and categorization in the second stage. By employing superior image feature selection and minimizing non-informative features, this proposed method achieves impressive accuracy metrics: 98.99% accuracy, 98.91% sensitivity, and 99.10% specificity. Furthermore, a comparative analysis is conducted between this method and six other architectures (Inception, InceptionResNetV2, ResNet101, ResNet152, VGG16, VGG19), with and without transfer learning. The findings demonstrate that the proposed method attains 98% accuracy on test data, without succumbing to overfitting.
Keywords
Natural Network
Convolution
Deep Learning
Covid 19
CT Scan Radiographs

Subjects

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International Journal of Web Research
Volume 6, Issue 2
July 2023
Pages 133-142