Diabetic Retinopathy Detection Using GLCM, Shi-Tomasi Corner Detection, and Random Forest Classifier
Keywords:
GLCM, DIARETB1, Shi-Tomasi and RF etc.Abstract
Patients with diabetes may develop clots, lesions, or hemorrhages in the area of the retina that is sensitive to light. This disease is known as diabetic retinopathy. High blood sugar causes blood vessels to become blocked, which encourages the production of new vessels and the creation of structures that resemble mesh. Evaluating the branching retinal vasculature is crucial for ophthalmologists to diagnose the condition effectively. In the process of assessing diabetic retinopathy, fundus scans of the eye undergo pre-processing and segmentation. For image preprocessing, various steps are undertaken, including enhancement, retinal mask extraction, blood vessel segmentation, optic disk extraction, and extraction of lesion candidate regions. To extract the branching blood vessels, thresholding technique is applied. Following this, morphological operations and adaptive histogram equalization are then applied to improve the image quality and remove areas that were falsely segmented. The proliferation of optical nerves was observed to be significantly greater in diabetic or affected patients compared to healthy individuals. Using a hybrid technique combining the Shi-Tomasi Corner Detector and GLCM, additional features are recovered from the lesion candidate. A random forest classifier is used to categorize the existence of diabetic retinopathy. Two datasets—DIARETDB1, a typical Diabetic Retinopathy Dataset, and a dataset from a medical facility including fundus scans of both normal and affected retinas—are used to assess the effectiveness of the proposed strategy. The experimental findings show how well the proposed method works in comparison to conventional approaches. When evaluated on the DIARETDB1 dataset, the model achieves an accuracy of 98.7% and a precision of 97.2%.
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