Detecção do Mycobacterium tuberculosis em imagens de baciloscopia de campo claro utilizando redes neurais convolutivas

Abstract

Tuberculosis (TB) is a disease caused by a slow-growing bacterium named Mycobacterium tuberculosis (MT). Since 2000 has been included among the top 10 leading causes of death worldwide. In 2015, Brazil ranked eighteenth in TB incidence, representing 9% and 33% of the estimated cases worldwide and for the Americas respectively. Light field smear microscopy is the most commonly used exam in developing countries for diagnosis and follow-up of the disease. Since 2008, several researches have been developed focused on TB bacillus detection, aiming the automation of light field smear microscopy. These studies used datasets with different amounts of images, explored different color aspects of bacilli, and applied Digital Image Processing and / or Machine Learning techniques, and more recently, Deep Learning using Grayscale images. However, Deep Learning techniques have not been explored using a robust smear microscopy image dataset that reflect real conditions of smear microscopy exams. This work presents a method for automatic detection of TB bacillus using Convolutional Neural Networks (CNN) using a dataset of images taken from 2 patients in RGB, R-G and Grayscale color formats. To reach the proposed goal, a patch dataset containing bacilli (positive patches) and without bacilli (negative patches) was generated. This patch dataset was used for training three different RNC architectures. Then, Non-maximum Suppression (NMS) algorithm was applied using CNN models and complete smear images for bacillus detection. Best results in the patch classification stage were obtained using R-G and RGB images and two CNN models, achieving an accuracy of 99% in both cases. At final stage, Bacilli detection in full smear images, best results were achieved using RGB images reaching a Precision of 56,82%, Recall of 86,15% and F1-score of 68,47%.