DR-NASNet: Automated System to Detect and Classify Diabetic Retinopathy Severity Using Improved Pretrained NASNet Model

Abstract

Diabetes is a widely spread disease that significantly affects people’s lives. The leading cause is uncontrolled levels of blood glucose, which develop eye defects over time, including Diabetic Retinopathy (DR), which results in severe visual loss. The primary factor causing blindness is considered to be DR in diabetic patients. DR treatment tries to control the disease’s severity, as it is irreversible. The primary goal of this effort is to create a reliable method for automatically detecting the severity of DR. This paper proposes a new automated system (DR-NASNet) to detect and classify DR severity using an improved pretrained NASNet Model. To develop the DR-NASNet system, we first utilized a preprocessing technique that takes advantage of Ben Graham and CLAHE to lessen noise, emphasize lesions, and ultimately improve DR classification performance. Taking into account the imbalance between classes in the dataset, data augmentation procedures were conducted to control overfitting. Next, we have integrated dense blocks into the NASNet architecture to improve the effectiveness of classification results for five severity levels of DR. In practice, the DR-NASNet model achieves state-of-the-art results with a smaller model size and lower complexity. To test the performance of the DR-NASNet system, a combination of various datasets is used in this paper. To learn effective features from DR images, we used a pretrained model on the dataset. The last step is to put the image into one of five categories: No DR, Mild, Moderate, Proliferate, or Severe. To carry this out, the classifier layer of a linear SVM with a linear activation function must be added. The DR-NASNet system was tested using six different experiments. The system achieves 96.05% accuracy with the challenging DR dataset. The results and comparisons demonstrate that the DR-NASNet system improves a model’s performance and learning ability. As a result, the DR-NASNet system provides assistance to ophthalmologists by describing an effective system for classifying early-stage levels of DR.

RDS-DR: An Improved Deep Learning Model for Classifying Severity Levels of Diabetic Retinopathy

“…Comparing the RDS-DR technique to well-established models like VGG19, VGG16, Inception V-3, and Xception and outperforming them in terms of accuracy further emphasizes the success of the proposed method. In our recent study, we have significantly surpassed the methodologies proposed by Sajid et al [2] in various aspects, including preprocessing, architectural design, performance metrics, image benchmarking, and computational efficiency. Unlike DR-NASNet [2], which utilized CLAHE and Bengraham techniques for image preprocessing, our approach incorporated the CLAHE technique paired with multi-scale retinex (MSR) for enhanced image feature refinement.…”

“…In our recent study, we have significantly surpassed the methodologies proposed by Sajid et al [2] in various aspects, including preprocessing, architectural design, performance metrics, image benchmarking, and computational efficiency. Unlike DR-NASNet [2], which utilized CLAHE and Bengraham techniques for image preprocessing, our approach incorporated the CLAHE technique paired with multi-scale retinex (MSR) for enhanced image feature refinement. Our analytical section delineates the prominent improvements achieved through these advancements.…”

“…Diabetic retinopathy is defined as the presence of ruptured vessels at the back of the retina. Color fundus retinal imaging can reveal DR symptoms including microaneurysm (MA), exudate (HE), hemorrhage (HM), and cotton wool spot (CWS) [2].…”