Diabetic Retinopathy Features Segmentation without Coding Experience with Computer Vision Models YOLOv8 and YOLOv9

• Published in: Vision (Basel) Vol. 8; no. 3; p. 48
• Main Authors: Rizzieri, Nicola, Dall’Asta, Luca, Ozoliņš, Maris
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.09.2024; MDPI
• Subjects: Algorithms; Artificial intelligence; Blood vessels; Computer vision; Datasets; Deep learning; Diabetes; Diabetes mellitus; Diabetic retinopathy; Disease; Early experience; Edema; Exudates; Eye diseases; Hemorrhage; Image processing; Lesions; Localization; Machine vision; Mathematical functions; Neural networks; retinal fundus; Retinopathy; Segmentation; YOLOv8; YOLOv9; YOLOv9; segmentation; diabetic retinopathy; retinal fundus; YOLOv8; computer vision
• ISSN: 2411-5150; 2411-5150
• DOI: 10.3390/vision8030048

Computer vision is a powerful tool in medical image analysis, supporting the early detection and classification of eye diseases. Diabetic retinopathy (DR), a severe eye disease secondary to diabetes, accompanies several early signs of eye-threatening conditions, such as microaneurysms (MAs), hemorrhages (HEMOs), and exudates (EXs), which have been widely studied and targeted as objects to be detected by computer vision models. In this work, we tested the performances of the state-of-the-art YOLOv8 and YOLOv9 architectures on DR fundus features segmentation without coding experience or a programming background. We took one hundred DR images from the public MESSIDOR database, manually labelled and prepared them for pixel segmentation, and tested the detection abilities of different model variants. We increased the diversity of the training sample by data augmentation, including tiling, flipping, and rotating the fundus images. The proposed approaches reached an acceptable mean average precision (mAP) in detecting DR lesions such as MA, HEMO, and EX, as well as a hallmark of the posterior pole of the eye, such as the optic disc. We compared our results with related works in the literature involving different neural networks. Our results are promising, but far from being ready for implementation into clinical practice. Accurate lesion detection is mandatory to ensure early and correct diagnoses. Future works will investigate lesion detection further, especially MA segmentation, with improved extraction techniques, image pre-processing, and standardized datasets.