A Convolutional Neural Network Approach for Diabetic Retinopathy Classification

Jiwani, Nasmin; Gupta, Ketan; Afreen, Neda

doi:10.1109/csnt54456.2022.9787577

Cited by 56 publications

(1 citation statement)

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“…Reduced dimensionality and feature extraction are performed with information from the machine learning archive at UCI and a deep learning model that uses principal component analysis (PCA). In [ 9 ], convolutional neural networks (CNNs) are proposed to automate the system for DR classification. The system, however, does not achieve high accuracies, and no novel image processing technique was proposed.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Related Workmentioning

confidence: 99%