Automated Classification Using End-to-End Deep Learning

Abstract

According to a study [1] by the Ministry of Health in Singapore, since 2009, cancer, ischaemic heart disease and pneumonia together accounted for approximately 60% of the total causes of death. It has been 9 years, and Pneumonia and other Acute Upper Respiratory Infections still is one of the top 10 conditions of hospitalization. In cases of respiratory diseases such as Chronic Obstructive Pulmonary Disease (COPD), it has been found that close to 55% of cases are misdiagnosed. This is shocking as, an early diagnosis of respiratory diseases can lead to an earlier treatment intervention, ultimately lessening symptoms, slowing the progression, and improving overall quality of life. With the advent of Deep Neural Network architectures which have shown phenomenal results in the field of Computer Assisted Diagnosis (CAD), we hope to implement a Lung Classification Model using End-to-End Deep learning to classify Chest X-Ray images into one of 14 primary classes of lung diseases. Using our implementation of the Densely Connected Convolutional Neural Network model architecture, we aim to increase existing model accuracy in Lung Disease classification by iteratively reducing the search space and region of interest for different. We shall experiment on a 14-class classification model and compare the results with a binary classifier as well, to understand the performance of DenseNets on Chest X-Ray (CXR) Data with a reduced search space.