Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks
This work addresses the problem of automating diabetic retinopathy diagnosis for medical applications, representing an incremental improvement over existing methods by integrating local and global features and handling data imbalance.
The authors tackled automated diabetic retinopathy analysis by proposing a two-stage deep convolutional neural network that detects lesion locations and types while grading severity, achieving performance comparable to trained human observers in lesion detection and significantly improving grading with an imbalanced weighting scheme.
We propose an automatic diabetic retinopathy (DR) analysis algorithm based on two-stages deep convolutional neural networks (DCNN). Compared to existing DCNN-based DR detection methods, the proposed algorithm have the following advantages: (1) Our method can point out the location and type of lesions in the fundus images, as well as giving the severity grades of DR. Moreover, since retina lesions and DR severity appear with different scales in fundus images, the integration of both local and global networks learn more complete and specific features for DR analysis. (2) By introducing imbalanced weighting map, more attentions will be given to lesion patches for DR grading, which significantly improve the performance of the proposed algorithm. In this study, we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus images from Kaggle competition dataset. Under the guidance of clinical ophthalmologists, the experimental results show that our local lesion detection net achieve comparable performance with trained human observers, and the proposed imbalanced weighted scheme also be proved to significantly improve the capability of our DCNN-based DR grading algorithm.