Enhancing Accuracy of Diabetic Retinopathy Detection Using a Hybrid Approach with the Fusion of Inceptionv3 and a Stacking Ensemble Learner

Abstract

Diabetic retinopathy (DR) is a severe global problem that affects millions of people worldwide and gets worse over time. If left untreated, DR can lead to blindness. Early and precise DR identification is necessary to address this developing challenge. Traditional approaches include applying machine learning or deep learning algorithms directly on the dataset or the preprocessed dataset, which has shown very good results recently. Very few focused on combining machine learning and deep learning-based algorithms. Extracting a good set of features is very crucial to getting higher performance from any machine learning-based or deep learning-based algorithm. This work presents a new method for DR detection by fusing a convolution neural network-based feature extraction method before feeding the data to a stacking ensemble learner, which uses several machine learning algorithms to make it more robust. Predictions from several classifiers, including decision trees, random forests, support vector machines, logistic regression, and others, have been used in previous studies of DR. In our work, we used these classifiers for our hybrid model. First, retinal image features are extracted using InceptionV3. Then, several fine-tuned machine learning-based classifiers have been used. Finally, all the classifier models are stacked together to create an ensemble model. Our hybrid approach showed promising performance in classifying binary (98.64%) and multi-class (94.95%) on the APTOS 2019 Blindness Detection dataset. This finding proves that our hybrid technique is more capable than the traditional approach for the early diagnosis of diabetic retinopathy and offers a great hope for better medical intervention.

Jagannath University Journal of Science, Volume 11, Number 1, June 2024, pp. 135−156