Pulmonary Tuberculosis Detection from Chest X-Ray Images Using Machine Learning

Abstract: One of today's deadliest diseases, tuberculosis (TB), is caused by "mycobacterium tuberculosis", which usually targets the lungs, due to weakening of immune system. Tuberculosis is very common, and if it is not detected, the patient's risk of death increases over time. Several computer diagnostic methods have been proposed to diagnose tuberculosis based on chest X-rays, as machine learning has been widely used in the field of image processing, especially deep learning. Adapting these machine learning techniques can provide more accurate, timely and reliable diagnostic results. Current research shows that manual diagnosis can be replaced by machine learning-based diagnosis with properly trained models, which provide more accurate results. DIP is becoming more prominent in the field of biomedicine. With image processing, a Support Vector Machine (SVM) learning model can be used to classify disabled lungs. The main objective of this paper is, to diagnose tuberculosis using machine learning trained models with chest x-ray images.