Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning

Abstract

Diabetic retinopathy (DR) has become one of the major causes of blindness. Due to the increased prevalence of diabetes worldwide, diabetic patients exhibit high probabilities of developing DR. There is a need to develop a labor-less computer-aided diagnosis system to support the clinical diagnosis. Here, we attempted to develop simple methods for severity grading and lesion detection from retinal fundus images. We developed a severity grading system for DR by transfer learning with a recent convolutional neural network called EfficientNet-B3 and the publicly available Kaggle Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 training dataset, which includes artificial noise. After removing the blurred and duplicated images from the dataset using a numerical threshold, the trained model achieved specificity and sensitivity values ≳ 0.98 in the identification of DR retinas. For severity grading, the classification accuracy values of 0.84, 0.95, and 0.98 were recorded for the 1st, 2nd, and 3rd predicted labels, respectively. The utility of EfficientNets-B3 for the severity grading of DR as well as the detailed retinal areas referred were confirmed via visual explanation methods of convolutional neural networks. Lesion extraction was performed by applying an empirically defined threshold value to the enhanced retinal images. Although the extraction of blood vessels and detection of red lesions occurred simultaneously, the red and white lesions, including both soft and hard exudates, were clearly extracted. The detected lesion areas were further confirmed with ground truth using the DIARETDB1 database images with general accuracy. The simple and easily applicable methods proposed in this study will aid in the detection and severity grading of DR, which might help in the selection of appropriate treatment strategies for DR.

Keywords: Computer-aided diagnosis; Convolutional neural network (CNN); Deep learning; Diabetic retinopathy (DR); Image processing; Lesion detection; Severity grading.