ABSTRACT
This article focuses on the detection of the Sars-Cov2 virus from a large-scale public human chest Computed Tomography (CT) scan image dataset using a customized convolutional neural network model and other convolutional neural network models such as VGG-16, VGG-19, ResNet 50, Inception v3, DenseNet, XceptionNet, and MobileNet v2. The proposed customized convolutional neural network architecture contains two convolutional layers, one max pooling layer, two convolutional layers, one max pooling layer, one flatten layer, two dense layers, and an activation layer. All the models are applied on a large-scale public human chest Computed Tomography (CT) scan image dataset. To measure the performance of the various convolutional neural network models, different parameters are used such as Accuracy, Error Rate, Precision, Recall, and F1 score. The proposed customized convolutional neural network architecture's Accuracy, Error Rate, Precision Rate, Recall, and F1 Score are 0.924, 0.076, 0.937, 0.921, and 0.926 respectively. In comparison with other existing convolutional neural network strategies, the performance of the proposed model is superior as far as comparative tables and graphs are concerned. The proposed customized convolutional neural network model may help researchers and medical professionals to create a full-fledged computer-based Sars-Cov-2 virus detection system in the near future. © 2023 IEEE.