Student Feedback Artificial Tree Algorithm-Based Recurrent Fuzzy LSTM Network for Diabetic Retinopathy Classification Using Fundus Images

A Deep Learning (DL)-based framework for Diabetic Retinopathy (DR) classification, named the Student Feedback Artificial Tree Algorithm-based Recurrent Fuzzy Long Short-Term Memory Network (SFATA-based RFLSTMN), is proposed in this study. First, the input image undergoes pre-processing using a Gaussian filter combined with Contrast-Limited Adaptive Histogram Equalization (CLAHE). Next, lesion segmentation is performed through deep joint[Formula: see text][Formula: see text][Formula: see text]U-Net, taking intensity-based distance into account. Subsequently, the feature extraction stage identifies and extracts the relevant features. Finally, DR classification is conducted using the RFLSTMN model, which integrates the strengths of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) architectures. Furthermore, RFLSTMN is trained by the Student Feedback Artificial Tree Algorithm (SFATA), which is developed through the integration of the Feedback Artificial Tree Algorithm (FATA) and Student Psychology-Based Optimization (SPBO). The performance assessment shows that the SFATA-based RFLSTMN model achieved superior results across all evaluation metrics, attaining an accuracy of 92.3%, sensitivity of 92.9%, specificity of 92.1%, precision of 91.5%, and [Formula: see text]1-score of 92.2%.