


Volume 20 No 20 (2022)
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Transfer Learning-Based Stack Ensemble Deep Learning Approach to Predict the Severity of Diabetic Retinopathy
Deva Kumar S, Venkatramaphanikumar S , Venkata Krishna Kishore Kolli
Abstract
Diabetic retinopathy (DR) is the most commonly occurring eye disorder and the main reason
underlying blindness in diabetics all around the world. Many technologies have emerged today
for the accurate diagnosis of DR at an early stage. Of these, deep learning (DL) is one of the most
effective methods. This research focuses on the prediction of DR severity into five classes,
Normal, Mild, Moderate, Severe, and Proliferative DR (PDR), using pre-trained models. Transfer
learning using models, such as EfficientNetB0, MobileNet, and Xception, were implemented with
customization. Further, the Stack Ensemble model was applied to combine the predictions of all
these pre-trained models using meta classifiers, such as Random Forest and Extra Trees Classifier
to grade the DR severity. The performance the proposed model was evaluated on the KAGGLE
and the Asia Pacific Tele-Ophthalmology Society (APTOS) retina datasets. The final outcome
revealed that the proposed model outperformed state-of-the-art pre-trained models, with an
accuracy of 0.96 and 0.97 on the KAGGLE and APTOS datasets, respectively.
Keywords
Diabetic Retinopathy, EfficientNetB0, MobileNet, Xception, Stack Ensemble, Random Forest Classifier, Extra Trees Classifier
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