Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning

Applied Sciences

Aun Irtaza¹  Tahira Nazir¹  Hafiz Malik²  Ali Javed³  Rizwan Ali Naqvi⁴  Dildar Hussain⁵ 

[1] Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan;Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA;Department of Software Engineering, University of Engineering and Technology, Taxila 47050, Pakistan;Department of Unmanned Vehicle Engineering, Sejong University, 209, Neundong-ro, Gwangjin-gu, Seoul 05006, Korea;School of Computational Sciences, Korea Institute for Advanced Study (KIAS), 85 Hoegiro Dongdaemun-gu, Soeul 02455, Korea;

关键词: glaucoma;    deep learning;    diabetic retinopathy;    fuzzy K-means clustering;    medical imaging;   

DOI  :  10.3390/app10186185

来源: DOAJ

【 摘 要 】

Diabetic patients are at the risk of developing different eye diseases i.e., diabetic retinopathy (DR), diabetic macular edema (DME) and glaucoma. DR is an eye disease that harms the retina and DME is developed by the accumulation of fluid in the macula, while glaucoma damages the optic disk and causes vision loss in advanced stages. However, due to slow progression, the disease shows few signs in early stages, hence making disease detection a difficult task. Therefore, a fully automated system is required to support the detection and screening process at early stages. In this paper, an automated disease localization and segmentation approach based on Fast Region-based Convolutional Neural Network (FRCNN) algorithm with fuzzy k-means (FKM) clustering is presented. The FRCNN is an object detection approach that requires the bounding-box annotations to work; however, datasets do not provide them, therefore, we have generated these annotations through ground-truths. Afterward, FRCNN is trained over the annotated images for localization that are then segmented-out through FKM clustering. The segmented regions are then compared against the ground-truths through intersection-over-union operations. For performance evaluation, we used the Diaretdb1, MESSIDOR, ORIGA, DR-HAGIS, and HRF datasets. A rigorous comparison against the latest methods confirms the efficacy of the approach in terms of both disease detection and segmentation.

【 授权许可】

Unknown