Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets

Screening and diagnosis of diabetic retinopathy disease is a well known problem in the biomedical domain. The use of medical imagery from a patient's eye for detecting the damage caused to blood vessels is a part of the computer-aided diagnosis that has immensely progressed over the past few years due to the advent and success of deep learning. The challenges related to imbalanced datasets, inconsistent annotations, less number of sample images and inappropriate performance evaluation metrics has caused an adverse impact on the performance of the deep learning models. In order to tackle the effect caused by class imbalance, we have done extensive comparative analysis between various state-of-the-art methods on three benchmark datasets of diabetic retinopathy: -Kaggle DR detection, IDRiD and DDR, for classification, object detection and segmentation tasks. This research could serve as a concrete baseline for future research in this field to find appropriate approaches and deep learning architectures for imbalanced datasets.

Automated summary

Screening and diagnosis of diabetic retinopathy disease is a significant problem in the biomedical domain. The use of medical imagery from a patient's eye for computer-aided diagnosis has greatly advanced with the success of deep learning. However, challenges with imbalanced datasets, inconsistent annotations, limited samples, and inappropriate evaluation metrics have impacted the performance of deep learning models.