Ensemble of CNN Models for Tuberculosis Diagnosis

Tuberculosis (TB) is curable, and millions of deaths could be averted if diagnosed early. One of the sources of screening for TB is chest x-rays. Still, its success depends on the interpretation of skilled and experienced radiologists, mostly lacking in high TB burden regions. However, with the intervention of a computer-aided detection system, TB can be automatically detected from chest x-rays. This paper presents an Ensemble model based on multiple pre-trained models to automatically detect TB from chest x-rays. The models were trained on the Shenzhen dataset and validated on the Montgomery dataset to achieve good generalization on a new (unseen) dataset. The proposed Ensemble model achieved high accuracy and sensitivity that is comparable with state-of-the-art models and outperformed existing Ensemble models aimed at Tuberculosis classification.