Tuberculosis detection and localization from chest x-ray images using deep convolutional neural networks

Abstract

Tuberculosis (TB) is a lung disease that is highly contagious and continues to be a major cause of death during the past few decades worldwide. As the most efficient and cost-effective imaging method for medical purposes, chest X-Rays (CXRs) have been widely used as the preliminary tool for diagnosing TB. The automatic detection of TB and the localization of suspected areas which contain the disease manifestations with high accuracy will greatly improve the general quality of the diagnosis processes. This thesis discusses and introduces some methods to improve the accuracy and stability of different deep convolutional neural network (CNN) models (VGG16, VGG19, Inception V3, ResNet34, ResNet50 and ResNet101) that are used for TB detection. The proposed method includes three major processes: modifications on CNN model structures, model fine-tuning via artificial bee colony algorithm, and the implementation of the ensemble CNN model. Comparisons of the overall performance are made for all three stages among various CNN models on three CXR datasets (Montgomery County Chest X-Ray dataset, Shenzhen Hospital Chest X-Ray dataset and NIH Chest X-Ray8 dataset). The tested performance includes the detection of abnormalities in CXRs and the diagnosis of different manifestations of TB. Moreover, class activation mapping is employed to visualize the localization of the detected manifestations on CXRs and make the diagnosis result visually convincing. The implementation of our proposed methods have the ability to assist doctors and radiologists in generating a well-informed decision during the detection of TB.