CXNet-m1: Anomaly Detection on Chest X-Rays With Image-Based Deep Learning

Shuaijing Xu, Hao Wu, Rongfang Bie
2019 IEEE Access  
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 than
more » ... re powerful than fine-tuning. We also raise a novel loss function sin-loss, which can learn discriminative information from misclassified and indistinguishable images. Besides, we optimize the convolutional kernels of CXNet-m1 to achieve better classification accuracy. The experimental results show that our light model CXNet-m1 with sin-loss function achieves better accuracy rate, recall rate, F1-score, and AUC value. It illustrates that designing a proper CNN is better than fine-tuning deep networks, and the increase of training data is vital to enhance the performance of CNN. INDEX TERMS Chest X-Rays image, anomaly detection, deep neural network, self-adapting loss function. 4466 2169-3536
doi:10.1109/access.2018.2885997 fatcat:kujbtpsrmveivh7b6h365aft5q