An Automated Diabetic Retinopathy Severity Level Classification using Vertically Stacked Squeeze Excitation Network with the Crayfish Optimization Algorithm

Diabetic retinopathy (DR) is a serious eye disease that can cause vision loss and blindness. This study proposes an advanced deep learning based DR classification approach for determining DR severity. Input images are collected from the APTOS-2019 dataset. Here, first images are pre-processed using adaptive Gaussian filter and then to increase quality of the images using the Modified Contrast Limited Interval-value Fuzzy Histogram Equalization (MCLI-FHE) method. Then, the image is segmented using a Patch Merging Swin Transformer (PMST). After segmentation, the important features get extracted using the Gray Level Dependence Matrix (GLDM) and Gray Level Co-occurrence Matrix (GLCM). Subsequently, the DR grouping is performed using Vertically Stacked Residual Convolutional Long short-term Enhanced Crayfish Squeeze Excitation Network (VSRCL-ECSEN). The Crayfish Optimization Algorithm (COA) is used for hyper parameter tuning. As a result, the proposed model accurately detects the corresponding lesions and classifies DR as mild, moderate, severe, proliferative, or no DR. For the APTOS-2019 dataset, the proposed approach achieved 99.4% accuracy, 98.6% precision, 98.1% sensitivity, 99% F1-score, 99.5% specificity, 0.00375 FPR, 0.015 FNR, 0.014 FDR, and 98.1% MCC results.