Diagnosis of diabetic retinopathy using machine learning & deep learning technique
This addresses the problem of time-consuming and error-prone manual analysis for healthcare providers, especially in remote areas, but appears incremental as it combines existing techniques.
The paper tackled automated diagnosis of diabetic retinopathy from fundus images by proposing a method combining YOLO_V8 for object detection to locate regions of interest and SVM for classification into DR stages, achieving 84% accuracy.
Fundus images are widely used for diagnosing various eye diseases, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. However, manual analysis of fundus images is time-consuming and prone to errors. In this report, we propose a novel method for fundus detection using object detection and machine learning classification techniques. We use a YOLO_V8 to perform object detection on fundus images and locate the regions of interest (ROIs) such as optic disc, optic cup and lesions. We then use machine learning SVM classification algorithms to classify the ROIs into different DR stages based on the presence or absence of pathological signs such as exudates, microaneurysms, and haemorrhages etc. Our method achieves 84% accuracy and efficiency for fundus detection and can be applied for retinal fundus disease triage, especially in remote areas around the world.