GANomaly-HRNet-based disease detection and classification of chest radiographs

Aiming at the problems of low recall and poor classification due to the complexity of chest X-ray lesion sites, this paper proposes a chest X-ray disease detection and classification method based on the chest dataset NIH Chest X-rays. The main work is: 1) Aiming at the problems such as low recall of the base model, the generative network of GANomaly is replaced by multi-scale coding and decoding U-Net, and at the same time, the memory-memory enhancement module is added between the coding and decoding networks; 2) Designing cascaded HRNet classification network under the premise of achieving high recall, and feature fusion is performed through global feature extraction and local feature extraction. Compared with the mainstream classification methods DenseNet, ResNet, and VGG, the cascaded HRNet method improves the average AUC by 7.9% and achieves the highest scores in most of the diseases; 3) Demonstrate the validity of the designed model through joint GANomaly-HRNet experiments on the dataset NIH Chest X-rays and the validation set of real hospital samples and accuracy.