A deep learning based model for diabetic retinopathy grading
Abstract Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy. Thus, the aim...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-87171-9 |
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| author | Samia Akhtar Shabib Aftab Oualid Ali Munir Ahmad Muhammad Adnan Khan Sagheer Abbas Taher M. Ghazal |
| author_facet | Samia Akhtar Shabib Aftab Oualid Ali Munir Ahmad Muhammad Adnan Khan Sagheer Abbas Taher M. Ghazal |
| author_sort | Samia Akhtar |
| collection | DOAJ |
| description | Abstract Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy. Thus, the aim of this research is to develop an automated and efficient system for early detection and accurate grading of diabetic retinopathy severity with less time consumption. In our research, we have developed a deep neural network named RSG-Net (Retinopathy Severity Grading) to classify DR into 4 stages (multi-class classification) and 2 stages (binary classification). The dataset utilized in this study is Messidor-1. In preprocessing, we have used Histogram Equalization to improve image contrast and denoising techniques to remove noise and artifacts which enhanced the clarity of the fundus images. We applied data augmentation techniques to our preprocessed images in order to tackle class imbalance issues. Augmentation techniques involve flipping, rotation, zooming and adjustment of color, contrast and brightness. The proposed RSG-Net model contains convolutional layers to perform automatic feature extraction from the input images and batch normalization layers to improve training speed and performance. The model also contains max pooling, drop out and fully connected layers. Our proposed RSG-Net model achieved a testing accuracy of 99.36%, specificity of 99.79% and a sensitivity of 99.41% in classifying diabetic retinopathy into 4 grades and it achieved 99.37% accuracy, 100% sensitivity and 98.62% specificity in classifying DR into 2 grades. The performance of RSG-Net is also compared with other state-of-the-art methodologies where it outperformed these methods. |
| format | Article |
| id | doaj-art-51a732a8954645078f951b6aec61ce60 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-51a732a8954645078f951b6aec61ce602025-02-02T12:16:22ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-025-87171-9A deep learning based model for diabetic retinopathy gradingSamia Akhtar0Shabib Aftab1Oualid Ali2Munir Ahmad3Muhammad Adnan Khan4Sagheer Abbas5Taher M. Ghazal6Department of Computer Science, Virtual University of PakistanDepartment of Computer Science, Virtual University of PakistanComputer Sciences Department, College of Arts & Science, Applied Science UniversitySchool of Computer Science, National College of Business Administration and EconomicsDepartment of Software, Faculty of Artificial Intelligence and Software, Gachon UniversityDepartment of Computer Science, Prince Mohammad Bin Fahd UniversityDepartment of Networks and Cybersecurity, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman UniversityAbstract Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy. Thus, the aim of this research is to develop an automated and efficient system for early detection and accurate grading of diabetic retinopathy severity with less time consumption. In our research, we have developed a deep neural network named RSG-Net (Retinopathy Severity Grading) to classify DR into 4 stages (multi-class classification) and 2 stages (binary classification). The dataset utilized in this study is Messidor-1. In preprocessing, we have used Histogram Equalization to improve image contrast and denoising techniques to remove noise and artifacts which enhanced the clarity of the fundus images. We applied data augmentation techniques to our preprocessed images in order to tackle class imbalance issues. Augmentation techniques involve flipping, rotation, zooming and adjustment of color, contrast and brightness. The proposed RSG-Net model contains convolutional layers to perform automatic feature extraction from the input images and batch normalization layers to improve training speed and performance. The model also contains max pooling, drop out and fully connected layers. Our proposed RSG-Net model achieved a testing accuracy of 99.36%, specificity of 99.79% and a sensitivity of 99.41% in classifying diabetic retinopathy into 4 grades and it achieved 99.37% accuracy, 100% sensitivity and 98.62% specificity in classifying DR into 2 grades. The performance of RSG-Net is also compared with other state-of-the-art methodologies where it outperformed these methods.https://doi.org/10.1038/s41598-025-87171-9Diabetic retinopathyConvolutional neural networkDeep learningAugmentationOptimization algorithm |
| spellingShingle | Samia Akhtar Shabib Aftab Oualid Ali Munir Ahmad Muhammad Adnan Khan Sagheer Abbas Taher M. Ghazal A deep learning based model for diabetic retinopathy grading Scientific Reports Diabetic retinopathy Convolutional neural network Deep learning Augmentation Optimization algorithm |
| title | A deep learning based model for diabetic retinopathy grading |
| title_full | A deep learning based model for diabetic retinopathy grading |
| title_fullStr | A deep learning based model for diabetic retinopathy grading |
| title_full_unstemmed | A deep learning based model for diabetic retinopathy grading |
| title_short | A deep learning based model for diabetic retinopathy grading |
| title_sort | deep learning based model for diabetic retinopathy grading |
| topic | Diabetic retinopathy Convolutional neural network Deep learning Augmentation Optimization algorithm |
| url | https://doi.org/10.1038/s41598-025-87171-9 |
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