Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing
This work addresses the problem of timely diagnosis for diabetic patients to prevent blindness, but it appears incremental as it builds on existing deep learning methods with optimizations.
The paper tackles early detection of diabetic retinopathy by proposing an enhanced deep learning model (EDLM) that processes retinal fundus images, achieving improved accuracy compared to several existing CNN architectures on a dataset of 3459 images.
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.