Classification of Chest XRay Diseases through image processing and analysis techniques
This work addresses disease diagnosis from chest X-rays, but it is incremental as it reviews and tests existing methods without introducing new techniques.
The paper compares multiple methods, including DenseNet121, for classifying diseases in chest X-ray images and deploys a web-based application, but does not report specific performance results or numbers.
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