Shaik Akbar,Divya Midhunchakkaravarthy,




Diabetic Retinopathy,Deep learning,Feature Extraction,Classification,


Diabetic retinopathy (DR) is one of the eye diseases that results in vision loss if not diagnosed earlier. The automated computer aided models on the DR images help in accurate treatment disease prevention. Microaneurysms (MA) and red spots are the indicators of DR for disease diagnosis. Many DR classification approaches have been proposed in the literature with deep learning framework and non-linear functionality. Also, these models are not applicable to large feature space due to high true negative rate. To optimize these problems, a hybrid feature selection based deep learning classifier is used to detect the MA and red spots disease severity on the large image dataset. In this paper, a new feature extraction approach is implemented to find the essential positive bag features to the deep learning framework. A hybrid SVM classification model is used to classify the disease patterns with high true positive rate. Experimental results are simulated on different DR image class labels; results show that the hybrid deep learning classification model is better than the traditional models under various statistical metrics on large dataset.


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