A GPU-based residual network for medical image classification in smart medicine

The recent advance of high-performance computing techniques like graphics processing unit (GPU) enables large-scale deep learning models for medical image analytics in smart medicine. Smart medicine has made great progress by applying convolutional neural networks (CNNs) like ResNet and VGG-16 to medical image classification. However, various CNN models achieve very limited accuracy in some cases where multiple diseases are revealed in an X-ray image. This paper presents a variant ResNet model by replacing the global average pooling with the adaptive dropout for medical image classification. In order for the presented model to recognize multiple diseases (i.e., multi-label classification), we convert the multi-label classification to N binary classification by training the parameters of the presented model for N times. Finally, experiments are conducted on a GPU Cluster to evaluate the presented model on three datasets, namely Montgomery County chest X-ray set, Shenzhen X-ray set, and NIH chest X-ray set. The results show the presented model achieves a great performance improvement for medical image classification without a significant efficiency reduction compared to the traditional architecture and VGG-16.