A Comparative Analysis of Machine Learning Algorithms for Detecting COVID-19 Using Lung X-Ray Images

Aug 5, 2026

Machine intelligence supports fast, accurate interpretation of clinical data, and COVID-19 has amplified the need for rapid, reliable decision support. Post-COVID syndrome (‘long COVID’) can persist for months, yet existing testing cannot diagnose, monitor, or measure recovery intervention efficacy. This study trains and compares machine-learning models using lung X-ray images, categorizing COVID-19 patients, pneumonia patients, and unaffected individuals. Performance is optimized through preprocessing, data augmentation, and hyperparameter tuning. Results show VGG-19 with augmentation achieves the best overall accuracy, enabling improved stratification for future post-COVID intervention research.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Methodology

Data preparation and labeling

Pre-processing and data augmentation

Model selection and training

Experimental Results

Comparative evaluation metrics

Best-performing model findings

Discussion and Future Work

A comparative analysis of machine learning algorithms for detecting COVID-19 using lung X-ray images Susmita Hamal a, Bhupesh Kumar Mishra b,∗, Robert Baldock c, William Sayers a, Tek Narayan Adhikari a, Ryan M. Gibson d a School of Engineering and Computing, University of Gloucestershire, UK b Data Science, AI & Modelling Centre (DAIM), University of Hull, UK c School of Pharmacy & Biomedical Sciences, University of Portsmouth, UK d Department of Computing, University of the West of Scotland, UK | A R T I C L E I N F O | | A B S T R A C T | | --- | --- | --- | | Keywords: Machine Learning Transfer Learning
Convolutional Neural Network Visual Geometry Group ResNet50
DenseNet201
Xception InceptionV3
Post-COVID syndrome Long COVID
Image classification | | Machine intelligence has the potential to play a significant role in diagnosing, managing, and guiding the treatment of disease, which supports the rising demands on healthcare to provide rapid and accurate interpretation of clinical data. The global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus (SARSCoV-2) exposed a need for rapid clinical data interpretation in response to an unprecedented burden on the healthcare system. A new healthcare challenge has arisen – post-COVID syndrome or ‘long COVID’. Symptoms of the post-COVID syndrome can persist for months following infection with SARS-CoV-2, often characterised by fatigue, breathlessness, dizziness, and pain. Despite this additional healthcare burden, no tests can diagnose, monitor, or determine the efficacy of treatments/interventions to support recovery. In this paper, an array of machine-learning algorithms is trained to evaluate and detect COVID-19-associated changes to lung tissue from X-ray images. X-ray images are classified from open sources into three categories: COVID- 19 patients, patients with pneumonia, and unaffected otherwise healthy individuals using existing Machine Learning (ML) and pre-trained deep learning models. Prioritising models with the fewest false positives and false negatives assessed the performance of different models in detecting COVID-19-associated lung tissue. In addition, image pre-processing, data augmentation, and hyperparameter tuning are used to achieve the best accuracy in the models. Different ML models, including K Nearest Neighbour (KNN), and decision trees (DT), as well as transfer learning models such as Convolutional Neural Network (CNN), Visual Geometry Group (VGG-16, VGG-19), ResNet50, DenseNet201, Xception, and InceptionV3, were tested to evaluate the performance of these models for X-ray images classification. The comparative analysis indicates that VGG-19 with augmentation performed best among the ten algorithms with a training accuracy of 99%, testing accuracy of 98%, and precision of 90% for COVID-19, 90% for normal, and 100% for pneumonia. This higher accuracy for detecting COVID-19-associated lung changes on X-ray may be further developed to stratify patients suffering from post-COVID syndrome. This may enable future intervention studies to determine the efficacy of treatments or better track patients’ prognoses to be optimised. | 1. Introduction In 2019, an outbreak of pneumonia initially spread in Wuhan city in China and the infectious agent was later named SARS-CoV-2 by the World Health Organization (WHO). SARS-CoV-2 is an enveloped virus with a positive sense, single-stranded ribonucleic acid (RNA) genome [1]. The virus predominantly affects the human respiratory system and is transferred readily from one person to another through coughing, sneezing and physical contact [2]. It can spread through touching or exposure to contaminated surfaces as the virus can survive for several days depending upon the surface and environment [3]. Primary symptoms of this disease include fever, dry cough, loss of taste and smell, sore throat, and muscle pain [4]. Despite significant research efforts to identify, characterise and limit

FAQs

What problem does the paper address about COVID-19 diagnosis?

It targets the need for rapid detection of COVID-19-associated lung changes from X-ray images and the additional challenge posed by post-COVID syndrome, for which effective monitoring and intervention evaluation remain limited.

How are the X-ray images classified in the study?

Images are classified into three categories: COVID-19 patients, pneumonia patients, and otherwise healthy unaffected individuals, using machine learning and pre-trained deep learning models.

Which model performed best and what results were reported?

VGG-19 with augmentation performed best among the evaluated algorithms, reaching 99% training accuracy and 98% testing accuracy, with precision reported as 90% for COVID-19 and normal and 100% for pneumonia.

Aug 5, 2026

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