Ensembles of Convolutional Neural Networks on Computer-Aided Pulmonary Tuberculosis Detection

Pulmonary tuberculosis is one of the top 10 causes of death in the world in which Latin America represents 3% of the worldwide incidence with a mortality rate of 7.3%. According to goals of World Health Organization to end the global tuberculosis epidemy by 2030, the development of novel diagnosis strategies is crucial. Taking this context into account, the present work shows results on the use of ensembles of Convolutional Neural Networks to aid Computer Vision diagnosis of tuberculosis from patients' chest X-ray. Our solution comprises an ensemble of three different deep architectures of such networks having accuracy higher than 93% on the proposed task. The solution was obtained and evaluated in real-world data from public datasets, favouring reproducibility, surpasses human experts performance reported by literature in these very same datasets, uses canonical architectures of convolutional neural networks, with and without Transfer Learning, and requires minimal effort on data preparation and no previous feature extraction.