Classification of Pneumonia and Tuberculosis from Chest X-rays

TL;DR

This paper presents an AI-based system for classifying pneumonia and tuberculosis from chest X-rays, achieving 92.97% accuracy in detecting abnormalities, aiding quicker diagnosis in medical settings.

Contribution

The study introduces a model capable of detecting two major chest diseases from X-rays with high accuracy, improving upon previous methods by identifying multiple abnormalities.

Findings

Achieved 92.97% accuracy in disease detection

Can distinguish between normal and abnormal X-rays

Detects pneumonia and tuberculosis simultaneously

Abstract

Artificial intelligence (AI) and specifically machine learning is making inroads into number of fields. Machine learning is replacing and/or complementing humans in a certain type of domain to make systems perform tasks more efficiently and independently. Healthcare is a worthy domain to merge with AI and Machine learning to get things to work smoother and efficiently. The X-ray based detection and classification of diseases related to chest is much needed in this modern era due to the low number of quality radiologists. This thesis focuses on the classification of Pneumonia and Tuberculosis two major chest diseases from the chest X-rays. This system provides an opinion to the user whether one is having a disease or not, thereby helping doctors and medical staff to make a quick and informed decision about the presence of disease. As compared to previous work our model can detect two types of abnormality. Our model can detect whether X-ray is normal or having abnormality which can be pneumonia and tuberculosis 92.97% accurately.