A Novel Architecture for Feature Extraction and Convolution for Image Segmentation of Pathology Detection from Chest X-Ray Images

Abstract

Neural networks are widely used for the automation of analysis and classification tasks in the field of medical image processing. They have successfully achieved state of the art performance in medical image segmentation and feature extraction techniques. This automatic classification in the medical field is very helpful in developing tools for early detection of dreadful pathologies, like tuberculosis and pneumonia, in areas where access to doctors or radiologists is scarce. In this work, we propose a novel approach for the classification of lung pathologies like tuberculosis and pneumonia by masking them in boundary boxes using convolutional neural networks. Our solution provides a flexible way, by using saved trained models that could be directly employed by the Radiologists. In this paper, we describe the architecture required to achieve such a scalable model which could be used by doctors and radiologists without too much training in the technologies of the times. The proposed convolutional architecture consists of connected components which are parallel residual blocks and sampling layers. The images do not lose their original quality, giving the best error free predictions. We visualize this model to be deployed in labs, providing access to medical imaging expertise to some of the most remote places in the world.