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Detecting anomaly of chest X-ray images by advanced technologies, such as deep learning, is an urgent need to improve the work efficiency and diagnosis accuracy. Fine-tuning existing deep learning networks for medical image processing suffers from over-fitting and low transfer efficiency. To overcome such limitations, we design a hierarchical convolutional neural network (CNN) structure for ChestX-ray14 and propose a new network CXNet-m1, which is much shorter, thinner but more powerful thandoi:10.1109/access.2018.2885997 fatcat:kujbtpsrmveivh7b6h365aft5q