ABSTRACT
Tuberculosis (TB) is one of the top 10 causes of death worldwide. The diagnosis and treatment of TB in its early stages is fundamental to reducing the rate of people affected by this disease. In order to assist specialists in the diagnosis in bright field smear images, many studies have been developed for the automatic Mycobacterium tuberculosis detection, the causative agent of Tb. To contribute to this theme, a method to bacilli detection associating convolutional neural network (CNN) and a mosaic-image approach was implemented. The propose was evaluated using a robust image dataset validated by three specialists. Three CNN architectures and 3 optimization methods in each architecture were evaluated. The deeper architecture presented better results, reaching accuracies values above 99%. Other metrics like precision, sensitivity, specificity and F1-score were also used to assess the CNN models performance.
