PneumoXttention: A CNN compensating for Human Fallibility when Detecting Pneumonia through CXR images with Attention

Abstract

Automatic Chest Radiograph X-ray (CXR) interpretation by machines is an important research topic. Pneumonia, a deadly disease, is diagnosed through CXRs and machine learning can accelerate this process. To this end, we present PneumoXttention, an algorithm that can detect pneumonia from a CXR image to compensate for human fallibility. The algorithm's architecture consists of an ensemble of two 13-layer convolutional neural networks trained on a dataset provided by the Radiological Society of North America, RSNA, containing 26,684 frontal X-ray images split into the categories of pneumonia and no pneumonia annotated by professional radiologists in North America. We validate PneumoXttention with impressive F1 scores on the test set, and against human radiologists on images drawn from RSNA and NIH, and also analyze PneumoXttention's usefulness in practice.