WENN-4: Weighted Ensemble for Enhanced Diabetic Retinopathy Detection

Diabetic Retinopathy (DR) is a serious eye condition that impacts individuals with diabetes, leading to retinal damage and potentially resulting in gradual vision loss. In this paper, we proposed a novel framework for diabetic retinopathy detection using deep learning techniques. The proposed framework employs an ensemble learning model named WENN-4 to classify fundus images into five classes and measure the severity of blindness of individuals. The proposed framework consists of following steps: customized preprocessing method that includes Gaussian Blurring and augmentation techniques, subsequently followed by inputting the processed images into diverse CNN architectures to extract discriminatory feature vectors for classification and early diagnoses and categorizes blindness severity levels, including no-DR, mild DR, severe, moderate, or Proliferative Diabetic Retinopathy (PDR). To enhance the overall performance of our proposed framework, we utilized an ensemble of models, WENN-4. This ensemble comprises ResNet-50, DenseNet-121, InceptionV3, and EfficientNetV2. This model achieves a remarkable accuracy of 93.7% accuracy on test data of APTOS 2019, surpassing existing methods. This ensemble of models lead to increased accuracy by leveraging complementary strengths, correcting individual misclassifications, and ultimately producing superior outcomes.