A Comparative Study of HARR Feature Extraction and Machine Learning Algorithms for Covid-19 X-Ray Image Classification

Aug 4, 2026

Upload by Patrick

The study evaluates how Harr feature extraction combined with multiple machine learning algorithms performs for COVID-19 X-ray image categorization. A dataset of 500 scans is used, including 250 COVID-19-positive cases and 250 healthy controls. Seven approaches are compared, including K-nearest neighbors, decision tree, linear regression, support vector machine, naive Bayes, random forest, and linear discriminant analysis. Performance is measured using metrics such as F1 score, precision, accuracy, recall, ROC area, and region of interest curves, showing SVM as the top performer (77%) and Naive Bayes as the lowest (58%).

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Motivation for early COVID-19 detection using X-ray imaging

Role of Harr feature extraction and machine learning algorithms

Trends in computer-aided diagnosis and automated feature extraction

A Comparative Study of HARR Feature Extraction and Machine Learning Algorithms for Covid-19 X Ray Image Classification Ahila T1, Dr A C Subha Jini2 1Research scholar, Department of computer Appplications NICHE, Kumaracoil, India e-mail: [ahilaanishraj@gmail.com](ahilaanishraj@gmail.com) 2Associate Professor, Department of Computer Appllications NICHE, Kumaracoil, India e-mail: [jinijeslin@gmail.com](jinijeslin@gmail.com) Abstract— In this study, we investigated how effectively COVID-19 image categorization using Harr feature extraction and machine learning algorithms. We were particularly interested in the effectiveness of these algorithms. A dataset of 500 X-ray scans, equally split between 250 COVID-19-positive cases and 250 healthy controls, served as the basis for our study. K-nearest neighbors,decision tree, Linear regression, support vector machine, regression, classification, naive Bayes,random forest, as well as linear discriminant analysis were among the seven machine-learning approaches used to categorize the photos. With the use of Harr feature extraction, the features of the pictures were extracted. We studied the efficacy of COVID-19 X-ray images for classification utilizing the combination of machine learning as well as the Harr feature extraction methods in the present investigation due to their effectiveness. We searched a database of 500 X-rays for this investigation, dividing them equally between groups of 250 patients with COVID-19-positive cases and 250 healthy people. Following that, the images were examined using seven various machine learning approaches for recognition. These methods included naive Bayes, linear discriminant analysis, random forests, classification,k-nearest neighbors, and regression trees. The information from the photos was gathered using the Harr feature extraction method. The effectiveness of the algorithms was evaluated with the help of a variety of metrics, such asF1 score, precision,accuracy, recall, the area under the ROC curve, and the region of interest curve. According to our research, the Support Vector Machine algorithm had the highest accuracy, at 77%, while the Naive Bayes approach had the lowest accuracy, at 58% . By using machine learning and Harr feature extraction approaches, the Random Forest method yields the best results, based on our research. The development of future COVID-19 X-ray image-based automated diagnostic systems may be influenced by these findings. Results from the suggested model were comparable to those of cutting-edge models trained using transfer learning techniques. The proposed model's main advantage is that it has ten times fewer parameters than the most advanced models.A receiver operating characteristic (ROC) curve's F1 score, and the algorithms' accuracy, precision, the area under the curve, and recall were all used as metrics. According to our findings, the Naive Bayes method gained the least accuracy (58%) and the Support Vector Machine method produced the highest accuracy (77%) when used. Our results reveal that employing Harr feature extraction and machine learning techniques, the Random Forest strategy is the most successful way to recognize COVID-19 X-ray pictures. These findings may be pertinent to the development of automated COVID-19 diagnosis tools relying on X-ray images. The recommended model produced results that were competitive when measured against cutting-edge models trained using transfer learning techniques. The suggested model employs 10 times fewer parameters than the most advanced models, which is its key selling point. Keywords-Support Vector Machine, Linear Discriminant Analysis, Covid-19, Xray, K-Nearest Neighbor, Native Bayes, Random Forest. I. INTRODUCTION Billions of human beings throughout the world have fallen victim to the extremely infectious COVID-19 virus. Early diagnosis of the disease is crucial for effective treatment and for reducing the spread of the virus [1] . X-ray imaging displ

FAQs

What dataset size and class balance were used for the COVID-19 X-ray classification study?

The study used 500 X-ray scans split evenly into 250 COVID-19-positive cases and 250 healthy controls.

Which machine learning algorithm achieved the highest and lowest accuracy in the experiments?

Support vector machine achieved the highest accuracy at 77%, while naive Bayes had the lowest accuracy at 58%.

How were model performances evaluated in the study?

Performance was assessed using metrics including F1 score, precision, accuracy, recall, the area under the ROC curve, and a region-of-interest related curve.

Aug 4, 2026

Upload by Patrick