Classification of Chest XRay Diseases through image processing and analysis techniques

TL;DR

This paper reviews various image processing techniques for classifying chest X-ray diseases, compares their performance, and introduces a web-based application to facilitate diagnosis, highlighting strengths and areas for improvement.

Contribution

It provides a comparative analysis of multiple methods including DenseNet121 for chest X-ray classification and offers an accessible web tool for practical use.

Findings

DenseNet121 shows promising accuracy in classification

The web application enables easy access for clinicians

Identifies weaknesses and suggests future improvements

Abstract

Multi-Classification Chest X-Ray Images are one of the most prevalent forms of radiological examination used for diagnosing thoracic diseases. In this study, we offer a concise overview of several methods employed for tackling this task, including DenseNet121. In addition, we deploy an open-source web-based application. In our study, we conduct tests to compare different methods and see how well they work. We also look closely at the weaknesses of the methods we propose and suggest ideas for making them better in the future. Our code is available at: https://github.com/AML4206-MINE20242/Proyecto_AML