Detection of Active and Latent Tuberculosis with Explainable Deep Learning Ensembles

Tuberculosis is one of the deadliest diseases in the world, despite being treatable, the number of new cases increases yearly. The situation is even more concerning due to antimicrobial resistance (AMR). To avoid undetected cases and breaking transmission, the research community is using artificial intelligence (AI) to develop new and fast diagnosis tools. Nevertheless, the creation of good quality and well-balanced datasets for training AI tools is challenging. It is especially difficult to collect data on asymptomatic patients who do not go to the professional as they do not feel the symptoms, such as the one who suffered latent tuberculosis. In this work, we propose an explainable deep learning ensemble of convolutional neural networks (CNNs) to classify tuberculosis chest X-ray (CXRs) images. The ensemble was designed to alleviate being influenced by unbalanced data due to the small number of latent patients included in the dataset. The system was trained to detect tuberculosis against healthy patients and other diseases with CXRs as the only input, so as to inform tuberculosis patients of the stage of the disease (active or latent). The model was designed with two parallel CNNs in an ensemble that used a random forest (RF) to overcome the imbalance. The system reported a performance of 96 % accuracy, showing that the ensemble can improve the performance of the underrepresented class. Further, the RF completed the interpretability of the system provided by grad-CAM heatmaps, supporting the evolution and integration of the system in clinical environments.