In recent decades, automatic diagnosis using machine-learning techniques have been the focus of research. Mycobacterium Tuberculosis (TB) is a deadly disease that has plagued most developing countries presents a problem that can be tackled by automatic diagnosis. The World Health Organization (WHO) set years 2030 and 2035 as milestones for a significant reduction in new infections and deaths although lack of well-trained professionals and insufficient or fragile public health systems (in developing countries) are just some of the major factors that have slowed the eradication of the TB endemic. Deep convolutional neural networks (DCNNs) have demonstrated remarkable results across problem domains dealing with grid-like data (i.e., images and videos). Traditionally, a methodology for detecting TB is through radiology combined with previous success DCNN have achieved in image classification makes them the perfect candidate to classify Chest X-Ray (CXR) images. In this study, we propose three types of DCNN trained using two public datasets and another new set which we collected from Konya Education and Research Hospital, Konya, Turkey. Also, the DCNN architectures were integrated with an extra layer called Spatial Pyramid Pooling (SPP) a methodology that equips convolutional neural networks with the ability for robust feature pooling by using spatial bins. The result indicates the potential for an automated system to diagnose tuberculosis with accuracies above a radiologist professional.
Spatial Pyramid Pooling in Deep Convolutional Networks for Automatic Tuberculosis Diagnosis
