Diagnosing Lung Disease Using Deep Learning - Intel AI

Research Using CheXNet at Stanford: CheXNet is a deep learning Convolutional Neural Network (CNN) model developed at Stanford University to identify thoracic pathologies from the NIH ChestXray14 dataset. CheXNet is a 121-layer CNN that uses chest X-Ray images to predict the output probabilities of a pathology. It correctly detects pneumonia by localizing the areas in the image that are most indicative of the pathology. Stanford researchers have been able to train the ChestX-Ray14 dataset using a pre-trained model of CheXNet-121 with the ImageNet2012-1K dataset. The NIH dataset consists of over one hundred thousand frontal chest X-ray images from over 30,000 unique patients that have been annotated with up to 14 thoracic diseases including pneumonia and emphysema.