Severity Classification of Diabetic Retinopathy Using Ensemble Stacking Method

Abstract

Diabetic retinopathy (DR), is a complication resulting from the disease that can lead to blindness if not detected early. Recently, many classification systems for diabetic retinopathy have been developed. However, several problems were found, namely, the classification results in certain classes still have less than optimal accuracy values, the lack of in-depth analysis for the results, and the overall accuracy that can still be improved. In this work, we experiment by evaluating and combining new deep learning models such as EfficientNet, EfficientNetV2, LCNet, MobileNetV3, TinyNet, and FBNetV3 using ensemble stacking techniques with four different meta-learners: decision trees, logistic regression, ANN, and SVM to provide better accuracy in classifying the severity of diabetic retinopathy. Our work offers satisfactory classification results on the APTOS 2019 dataset with training, validation, testing, and F1 score accuracy of 96.56%, 95.33%, 84.17%, and 70.16%, respectively.

Apache Spark for Analysis of Electronic Health Records: A Case Study of Diabetes Management

“…The results are forwarded back to the main node for additional processing when the sub-issues have been resolved [18]. The use of big data analytics in the diabetic retinopathy healthcare sector has increased recently, with Apache Spark engine as a key tool for processing and analyzing enormous amounts of healthcare data [19][20][21][22][23].…”