TUBERCULOSIS DIAGNOSIS USING X-RAY IMAGES

Tuberculosis (TB) is caused by the bacteria Mycobacterium tuberculosis. It most often affects the lungs. Tuberculosis is a preventable and curable disease. The Global Annual TB report, 1.5 million TB related deaths were reported in 2015. In 2016, this increased with 1.7 million reported deaths and more than 10 million people infected with the disease. The objective of this work is to analyze medical X-ray images using deep learning methods and explore images to achieve classification of Tuberculosis. The Convolutional Neural Networks (CNN) algorithm based deep learning classification approaches has been chosen as it has the ability to intrinsically extract the low level representations from data using little pre-processing in comparison with other image classification algorithms. This simple and efficient model will lead clinicians towards better diagnostic decisions for patients to provide them solutions with good accuracy for medical imaging. Supervised learning algorithms convolutional neural networks (CNN) were considered for the classification task. The performance of the designed model is measured on two publicly available datasets: the Montgomery County chest X-ray (MC) and Shenzhen chest X-ray set. It achieves accuracy of 90% and 80% respectively on these datasets.