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We develop an algorithm that can detect pneumonia from chest X-rays at alevel exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layerconvolutional neural network trained on ChestX-ray14, currently the largestpublicly available chest X-ray dataset, containing over 100,000 frontal-viewX-ray images with 14 diseases. Four practicing academic radiologists annotate atest set, on which we compare the performance of CheXNet to that ofradiologists. We find that CheXNet exceeds average radiologist performance onthe F1 metric. We extend CheXNet to detect all 14 diseases in ChestX-ray14 andachieve state of the art results on all 14 diseases.

CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning