Convolutional neural network modeling for classification of pulmonary tuberculosis disease

Abstract

Tuberculosis (TB) is caused by bacteria (Mycobacterium tuberculosis) that most often affect the lungs so called Pulmonary Tuberculosis or PTB. Diagnosis using a chest radiograph image manually by doctor requires a long time, even difficult to detect PTB. Convolutional neural network (CNN) is a deep learning method that adopts the performance of human brain neurons called neural network and convolution functions to classify images. CNN can also help classify PTB based on chest radiograph images. This study uses data from the National Library of Medicine, Maryland, USA in collaboration with Shenzhen No.3 People's Hospital, Guangdong Medical College, Shenzhen, China including 663 images entered into two classes, normal and PTB. This study uses adaptive momentum optimization (Adam) which serves to improve the accuracy of the model. The classification results of the models built were 99.19% for training data and 80.60% for validation data with 75 epochs, and accuracy in the test data was 84% which means that the model was able to qualify 84% of the test data into normal classes and PTB appropriately. 25 correctly classified as normal lungs, 5 incorrectly and 26 correctly classified as PTB and 5 incorrect.