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Diabetic Retinopathy (DR) is an eye disease that is the main cause of blindness in developed countries. Treatment of DR and prevention of blindness depend heavily on regular monitoring, early-stage diagnosis, and timely treatment. Vision loss can be effectively prevented by the automated diagnostic system that assists ophthalmologists who otherwise practice manual lesion detection processes which are tedious and time-consuming. Therefore, the purpose of this research is to design a system that can detect the presence of DR and be able to classify it based on its severity. In this proposed, the classification process is carried out based on image discovery by extracting GLCM texture features from 454 retinal fundus images in the IDRID database which are classified into 4 severity levels, namely normal, mild NPDR, moderate NPDR, and severe NPDR. The features obtained from each image will be used as input for the classification process using SVM. As a result, the classification system that has been trained is able to classify 4 levels of DR severity with an average accuracy of 89.55%, a sensitivity of 81.03%, and a specificity of 92.89%. Based on the results of the evaluation of the performance of this classification system, it can be concluded that the specificity value is higher than the specificity value, this indicates that the system that has been trained has a good ability to identify negative samples or those that indicate a class. |
