Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model

• Published in: IEEE access Vol. 10; pp. 73079 - 73086
• Main Authors: Abbood, Saif Hameed, Hamed, Haza Nuzly Abdull, Rahim, Mohd Shafry Mohd, Rehman, Amjad, Saba, Tanzila, Bahaj, Saeed Ali
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; Algorithms; Angiography; Blurring; Deep learning; Diabetes; Diabetes mellitus; Diabetic retinopathy; Diagnosis; Feature extraction; fundus image; health risks; healthcare; Histograms; Image analysis; Image classification; Image color analysis; Image contrast; Image enhancement; Image quality; Image segmentation; Machine learning; Medical imaging; Noise reduction; Ophthalmology; Retina; Retinal images
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3189374

Diabetic Retinopathy (DR) is a prevalent acute stage of diabetes mellitus that causes vision-effecting abnormalities on the retina. This will cause blindness if not identified early. Because DR not an irreversible procedure, and only vision is preserved via care. Consequently, Early diagnosis and care with DR will significantly minimize the chance of vision loss. In modern ophthalmology, retinal image analysis has become a popular approach to disease diagnosis. The ophthalmologists and computerized systems extensively employ fundus angiography to detect DR-based clinical signs for early detection of DR. fundus photographs are commonly prone to low contrast, noise, and irregular illumination issues due to the complexity of imaging environments such as imaging variety of angles and light conditions. This research presents an Algorithm for improving the quality of images to strengthen the standard of color fundus images by reducing the noise and improving the contrast. The approach includes two main stages: cropping the images to remove insignificant content, then applying the shape crop and gaussian blurring for noise reduction and contrast improvement. The experimental results are evaluated using two standard datasets EyePACS and MESSIDOR. It's clearly shown that the outcomes of feature extraction and classification of enhanced images is outperform the results without applying the enhancement approach. The improved algorithm is also tested in smart hospitals as an IoMT application.