Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder
Abstract Diabetic retinopathy (DR) presents a significant concern among diabetic patients, often leading to vision impairment or blindness if left untreated. Traditional diagnosis methods are prone to human error, necessitating accurate alternatives. While various computer-aided systems have been de...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85752-2 |
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| author | Shagufta Almas Fazli Wahid Sikandar Ali Ahmed Alkhyyat Kamran Ullah Jawad Khan Youngmoon Lee |
| author_facet | Shagufta Almas Fazli Wahid Sikandar Ali Ahmed Alkhyyat Kamran Ullah Jawad Khan Youngmoon Lee |
| author_sort | Shagufta Almas |
| collection | DOAJ |
| description | Abstract Diabetic retinopathy (DR) presents a significant concern among diabetic patients, often leading to vision impairment or blindness if left untreated. Traditional diagnosis methods are prone to human error, necessitating accurate alternatives. While various computer-aided systems have been developed to assist in DR detection, there remains a need for accurate and efficient methods to classify its stages. In this study, we propose a novel approach utilizing enhanced stacked auto-encoders for the detection and classification of DR stages. The classification is performed across one healthy (normal) stage and four DR stages: mild, moderate, severe, and proliferative. Unlike traditional CNN approaches, our method offers improved reliability by reducing time complexity, minimizing errors, and enhancing noise reduction. Leveraging a comprehensive dataset from KAGGLE containing 35,126 retinal fundus images representing one healthy (normal) stage and four DR stages, our proposed model demonstrates superior accuracy compared to existing deep learning algorithms. Data augmentation techniques address class imbalance, while SAEs facilitate accurate classification through layer-wise unsupervised pre-training and supervised fine-tuning. We evaluate our model’s performance using rigorous quantitative measures, including accuracy, recall, precision, and F1-score, highlighting its effectiveness in early disease diagnosis and prevention of blindness. Experimental results across different training/testing ratios (50:50, 60:40, 70:30, and 75:25) showcase the model’s robustness. The highest accuracy achieved during training was 93%, while testing accuracy reached 88% on a training/testing ratio of 75:25. Comparative analysis underscores the model’s superiority over existing methods, positioning it as a promising tool for early-stage DR detection and blindness prevention. |
| format | Article |
| id | doaj-art-563cbfab819e4b7fb4681e62d7a6cb8f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-563cbfab819e4b7fb4681e62d7a6cb8f2025-01-26T12:23:51ZengNature PortfolioScientific Reports2045-23222025-01-0115113110.1038/s41598-025-85752-2Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoderShagufta Almas0Fazli Wahid1Sikandar Ali2Ahmed Alkhyyat3Kamran Ullah4Jawad Khan5Youngmoon Lee6Department of Information Technology, The University of HaripurDepartment of Information Technology, The University of HaripurDepartment of Information Technology, The University of HaripurCollege of Technical Engineering, The Islamic UniversityDepartment of Biology, The University of HaripurSchool of Computing, Gachon UniversityDepartment of Robotics, Hanyang UniversityAbstract Diabetic retinopathy (DR) presents a significant concern among diabetic patients, often leading to vision impairment or blindness if left untreated. Traditional diagnosis methods are prone to human error, necessitating accurate alternatives. While various computer-aided systems have been developed to assist in DR detection, there remains a need for accurate and efficient methods to classify its stages. In this study, we propose a novel approach utilizing enhanced stacked auto-encoders for the detection and classification of DR stages. The classification is performed across one healthy (normal) stage and four DR stages: mild, moderate, severe, and proliferative. Unlike traditional CNN approaches, our method offers improved reliability by reducing time complexity, minimizing errors, and enhancing noise reduction. Leveraging a comprehensive dataset from KAGGLE containing 35,126 retinal fundus images representing one healthy (normal) stage and four DR stages, our proposed model demonstrates superior accuracy compared to existing deep learning algorithms. Data augmentation techniques address class imbalance, while SAEs facilitate accurate classification through layer-wise unsupervised pre-training and supervised fine-tuning. We evaluate our model’s performance using rigorous quantitative measures, including accuracy, recall, precision, and F1-score, highlighting its effectiveness in early disease diagnosis and prevention of blindness. Experimental results across different training/testing ratios (50:50, 60:40, 70:30, and 75:25) showcase the model’s robustness. The highest accuracy achieved during training was 93%, while testing accuracy reached 88% on a training/testing ratio of 75:25. Comparative analysis underscores the model’s superiority over existing methods, positioning it as a promising tool for early-stage DR detection and blindness prevention.https://doi.org/10.1038/s41598-025-85752-2Diabetic retinopathyDisabilityDeep learningStacked auto-encoderDropout |
| spellingShingle | Shagufta Almas Fazli Wahid Sikandar Ali Ahmed Alkhyyat Kamran Ullah Jawad Khan Youngmoon Lee Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder Scientific Reports Diabetic retinopathy Disability Deep learning Stacked auto-encoder Dropout |
| title | Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder |
| title_full | Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder |
| title_fullStr | Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder |
| title_full_unstemmed | Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder |
| title_short | Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder |
| title_sort | visual impairment prevention by early detection of diabetic retinopathy based on stacked auto encoder |
| topic | Diabetic retinopathy Disability Deep learning Stacked auto-encoder Dropout |
| url | https://doi.org/10.1038/s41598-025-85752-2 |
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