A Study On Tuberculosis Classification In Chest X-Ray Using Deep Residual Attention Networks

The introduction of deep learning techniques for the computer-aided detection scheme has shed a light for real incorporation into the clinical workflow. In this work, we focus on the effect of attention in deep neural networks on the classification of tuberculosis x-ray images. We propose a Convolutional Block Attention Module (CBAM), a simple but effective attention module for feed-forward convolutional neural networks. Given an intermediate feature map, our module infers attention maps and multiplied it to the input feature map for adaptive feature refinement. It achieves high precision and recalls while localizing objects with its attention. We validate the performance of our approach on a standard-compliant data set, including a dataset of 4990 x-ray chest radiographs from three hospitals and show that our performance is better than the models used in previous work.