Diabetic retinopathy detection and grading system using deep learning approach.

Early screening for diabetic retinopathy (DR) prevents vision loss in diabetic patients. Objective: To classify DR cases, this study suggests computer-assisted screening and diagnosis. The proposed methodology consists of retinal blood vessel, macular region, and exudate segmentation. In this study, exudates were screened, and DR was classified as mild, moderate, or severe by analyzing the presence of exudates in the macular region. Methods: The bit-plane morphological slicing technique was employed to segment the macular region. The U-Net deep learning approach was used to segment the exudates from the retinal images, and the proposed methodologies were applied and evaluated on retinal images in the publicly available Methods to Evaluate Segmentation and Indexing Techniques in the field of Retinal Ophthalmology (MESSIDOR) and High-Resolution Fundus (HRF) datasets. Finally, DR can be diagnosed by analyzing the presence of exudates in the macular region, and DR can be diagnosed as mild, moderate, or severe based on the severity levels. The conventional segmentation algorithms segment and locate the internal boundary of pixels in both exudates and macula. In contrast with the conventional segmentation methods, the proposed algorithm segments and locates both internal and external boundary of pixels in both exudates and macula region which increases the segmentation accuracy. Results: The proposed macular region segmentation method obtained 98.9% SeI, 99.2% SpI, and 99.1% AccI for the MESSIDOR dataset. The proposed macular region segmentation method obtained 99.2% SeI, 99.7% SpI, and 99.8% AccI for the HRF dataset. The proposed exudate segmentation method obtained 99.3% SeI, 99.2% SpI, and 99.2% AccI for the MESSIDOR dataset. The proposed exudate segmentation method obtained 99.7% SeI, 99.3% SpI, and 99.1% AccI for the HRF dataset. Conclusion: The segmentation time period of both exudates and macula region in retinal image is low when comparing with other segmentation methods. Moreover, this proposed segmentation and diagnosis method was tested with other DIARETDB1, Retinal Fundus Multi-disease Image, and Fundus Image Registration Dataset datasets to validate the effectiveness of the proposed method.