Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy, which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying the lesions are high. Similarly, training for Convolutional Neural Networks is more time-consuming. This proposed research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model for timely DR identification that is potentially more accurate than existing CNN-based models. The proposed model will detect various lesions from retinal images in the early stages. First, characteristics are retrieved from the retinal fundus picture and put into the EDLM for classification. For dimensionality reduction, EDLM is used. Additionally, the classification and feature extraction processes are optimized using the stochastic gradient descent optimizer. The EDLM effectiveness is assessed on the KAG GLE dataset with 3459 retinal images, and results are compared over VGG16, VGG19, RESNET18, RESNET34, and RESNET50.

Improving Classification of Retinal Fundus Image Using Flow Dynamics Optimized Deep Learning Methods

Diabetic Retinopathy (DR) refers to a barrier that takes place in diabetes mellitus damaging the blood vessel network present in the retina. This may endanger the subjects' vision if they have diabetes. It can take some time to perform a DR diagnosis using color fundus pictures because experienced clinicians are required to identify the tumors in the imagery used to identify the illness. Automated detection of the DR can be an extremely challenging task. Convolutional Neural Networks (CNN) are also highly effective at classifying images when applied in the present situation, particularly compared to the handmade and functionality methods employed. In order to guarantee high results, the researchers also suggested a cutting-edge CNN model that might determine the characteristics of the fundus images. The features of the CNN output were employed in various classifiers of machine learning for the proposed system. This model was later evaluated using different forms of deep learning methods and Visual Geometry Group (VGG) networks). It was done by employing the images from a generic KAGGLE dataset. Here, the River Formation Dynamics (RFD) algorithm proposed along with the FUNDNET to detect retinal fundus images has been employed. The investigation's findings demonstrated that the approach performed better than alternative approaches.