NetraDeep: An Integrated Deep Learning and Image Processing System for Precise Detection of Hard Exudates

Abstract

Hard Exudate (HE) is a common manifestation of various eye diseases, such as diabetic retinopathy (DR), and a prominent cause of vision loss and blindness. Researchers aim to visualize and quantify these exudates using deep learning (DL) and image processing (IP) models from retinal images. However, the requirement for a large number of labelled image datasets for DL models to work on diverse and poor-quality images makes this task challenging. To address this challenge, we introduce NetraDeep, a system that integrates data-driven DL and rule-based IP techniques for exudate segmentation. Our system uses IP models to detect and extract some features and assists DL models in detecting more advanced features and vice versa. The IP models are rule-based and use predefined rules to process images, while the DL models are data-driven and learn from the input data. NetraDeep provides visual and quantitative assessments while mitigating noise and other confounding factors such as artifacts and noise. The training of DL models of this system requires only a limited number of labelled fundus images from publicly available datasets. It provides accurate pixel-wise segmentation results on the public and private image datasets collected from local eye hospitals. Through extensive evaluation, our system achieved remarkable performance, with a dice coefficient of \(0.84\) for the public dataset and a rating of \(9.78\) and \(9.43\) out of 10, as corroborated by two medical experts with experience of more than 20 and 5 years, respectively, for the private image dataset.