Designing a Visual Geometry Group-based Triad-Channel Convolutional Neural Network for COVID-19 Prediction

Bashiri Mosavi, Seyed Alireza; Khalaf Beigi, Omid; Mahjoubifard, Arash

doi:10.22044/jadm.2024.15205.2626

Document Type : Original/Review Paper

Authors

¹ Department of Electrical and Computer Engineering, Buein Zahra Technical University, Buein Zahra, Qazvin, Iran.

² Department of Electrical and Computer Engineering, Kharazmi University, Tehran, Iran.

³ Department of Computer Engineering and Information Technology, University of Qom, Qom, Iran.

10.22044/jadm.2024.15205.2626

Abstract

Using intelligent approaches in diagnosing the COVID-19 disease based on machine learning algorithms (MLAs), as a joint work, has attracted the attention of pattern recognition and medicine experts. Before applying MLAs to the data extracted from infectious diseases, techniques such as RAT and RT-qPCR were used by data mining engineers to diagnose the contagious disease, whose weaknesses include the lack of test kits, the placement of the specialist and the patient pointed at a place and low accuracy. This study introduces a three-stage learning framework including a feature extractor by visual geometry group 16 (VGG16) model to solve the problems caused by the lack of samples, a three-channel convolution layer, and a classifier based on a three-layer neural network. The results showed that the Covid VGG16 (CoVGG16) has an accuracy of 96.37% and 100%, precision of 96.52% and 100%, and recall of 96.30% and 100% for COVID-19 prediction on the test sets of the two datasets (one type of CT-scan-based images and one type of X-ray-oriented ones gathered from Kaggle repositories).

Keywords

Main Subjects

H.3. Artificial Intelligence

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Designing a Visual Geometry Group-based Triad-Channel Convolutional Neural Network for COVID-19 Prediction

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Volume 12, Issue 3July 2024Pages 423-434

Volume 12, Issue 3
July 2024
Pages 423-434