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    Diabetic retinopathy detection using developed hybrid cascaded multi-scale DCNN with hybrid heuristic strategy
    (2024) Tabtaba, Ahlam Asadig Ali; Ata, Oğuz
    In recent times, Diabetic Retinopathy (DR) is the most inevitable ailment caused by high blood sugar levels in humans. Conversely, the detection process is accomplished by many learning algorithms. Some models have used the single view of retinal images, it renders unsatisfactory outcomes to forecast the disorder. Due to inadequate of retinal lesion features, the system gradually degrades the performance, and the system has a fewer tendencies to diagnose the disease. On the other hand, the computer-based model is suggested to detect the DR. Nevertheless, these methods are most cost and computationally effective and behinds with feature representation, futile to classify the diseases. To conquer against these shortcomings, a novel DR diagnosis model is proposed using a hybrid heuristic-aided deep learning model with fundus images. The retinal fundus images are collected that are given to the pre-processing stages as scaling, cropping and "Contrast Limited Adaptive Histogram Equalization (CLAHE)". In image augmentation, the high quality image is given for further processing with the help of Generative Adversarial Networks (GANs). Subsequently, the augmented images are fed into the model of a Hybrid cascaded Multi-scale Dilated Convolutional Neural Network (HCMD-CNN), where the Residual Attention Network (RAN) and the MobileNet are integrated to provide promising results for DR detection. Furthermore, the parameters present inside the HCMD-CNN are optimized to achieve higher performance with the help of newly designed Modified Sooty Tern Golden Eagle Optimization (MSTGEO). The implementation results will be analyzed through existing DR detection schemes to ensure the efficiency of the suggested DR detection model.