An efficient machine learning approach for classification of diabetic retinopathy stages

Abstract

Diabetic retinopathy (DR) the prime cause of blindness, develops when glucose levels rise, causing retinal damage. DR can be prevented if the illness is detected early. As a result, early grading, categorization, and diagnosis of DR can help diabetic patients avoid visual loss. Several system methods assist in the classification of DR using high-performance criteria. This work proposes an efficient system-based DR classification. The purpose of efficient machine learning dabetic retinopatyy grading classification (EML-DRGC) design is to recognize DR impulsively with highest accuracy. The proposed technique employs preprocessing methods such as employing the Gaussian filtering approach for removing noise present in retinal fundus images. The segmentation process is followed using K-means segmentation algorithm which is used for segmenting the region of interest (ROI) from background. Moreover, Feature extraction process is done by using gray level co-occurrence matrix (GLCM) in which features are extracted bycapturing the image's visual content and features from acceerated segment test (FAST) design is used as extractor of features. Finally, multi support vector machine is utilized as classifier for detecting severity levels of DR. Performance metrics such as accuracy of 98.38% and specificity of 98.34% are obtained which are superior to existing designs.