Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced
  Feature Extraction Processing
V. Banupriya S. Anusuya
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.