Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection

TL;DR

This paper compares various neural network models for classifying X-ray images to detect COVID-19, highlighting DenseNet's superior accuracy and other models' high precision in a comprehensive analysis.

Contribution

It provides a comparative evaluation of eight pre-trained neural networks for COVID-19 detection in X-ray images, including accuracy, precision, and heat map analysis.

Findings

DenseNet achieved 97.64% accuracy in multiclass classification.

VGG, ResNet, and MobileNet reached 99.98% precision in binary classification.

Heat map analysis was used for model interpretability.

Abstract

This study presents a comparative analysis of methods for detecting COVID-19 infection in radiographic images. The images, sourced from publicly available datasets, were categorized into three classes: 'normal,' 'pneumonia,' and 'COVID.' For the experiments, transfer learning was employed using eight pre-trained networks: SqueezeNet, DenseNet, ResNet, AlexNet, VGG, GoogleNet, ShuffleNet, and MobileNet. DenseNet achieved the highest accuracy of 97.64% using the ADAM optimization function in the multiclass approach. In the binary classification approach, the highest precision was 99.98%, obtained by the VGG, ResNet, and MobileNet networks. A comparative evaluation was also conducted using heat maps.