On the Use of Conventional Neural Networks for COVID-19 Detection in CT-Scan Images: A Comparative Study and Performance Analysis

Menglin Niu, Larbi Boubchir, Boubaker Daachi

Abstract: This paper presents a comprehensive study on deep learning for COVID-19 detection using CT-scan images. The proposed study investigates several Conventional Neural Networks (CNN) architectures such as AlexNet, ZFNet, VGGNet, and ResNet, and thus proposed a hybrid methodology base on merging the relevant optimized architectures considered for detecting COVID-19 from CT-scan images. The proposed methods have been assessed on real datasets, and the experimental results conducted have shown the effectiveness of the proposed methods, allowing achieving a higher accuracy up to 99%.