| dc.description.abstract | According to the World Health Organization, tuberculosis (along with AIDS) is the most
deadly infectious disease in the world. In 2014 it is estimated that 1.5 million people infected by the Mycobacterium Tuberculosis died, most of them in developing countries. Many of those deaths could have been prevented if the disease was detected at an earlier stage, but unfortunately the most advanced diagnosis methods are cost prohibitive for mass adoption in developing countries. One of the most popular tuberculosis diagnosis methods still is by analysis of frontal thoracic radiographies, however the impact of this method is diminished by the need for individual analysis of each radiography by properly trained radiologists. On the other hand, there is significant research on automating diagnosis by the application of computational techniques to lung radiographic images, eliminating the need for individual analysis of the radiographies and greatly diminishing the cost. In addition to that, recent improvements on Convolutional Neural Networks, which are related to Deep Learning, accomplished excellent results classifying images on diverse domains, but it’s application for tuberculosis diagnosis still is limited. Thus, the focus of this work is to produce an investigation that will advance the research in the area, proposing three approaches to the application of Convolutional Neural Networks to detect the disease. The three proposals presented in this works are implemented and compared to the current literature. The obtained results are competitive with works published in the area,
achieving superior results in most cases, thus demonstrating the great potential of Convolutional Networks as medical image feature extractors. | en |
