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

Source: [Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection](<https://www.sota2.com/research/paper/68fa768d1e209131cea6dab1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:56:26.552Z

Authors: Jimi Togni, Romis Attux

Published: 2025-01-08T00:17:03.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.04196>)

[PDF](<https://arxiv.org/pdf/2501.04196.pdf>)