Detection of Diabetic Retinopathy Using a Multi-Decision Inception-ResNet-Blended Hybrid Model
Diabetic retinopathy (DR) is a severe complication of diabetes that affects the retinal structures and can lead to significant visual impairment or even blindness. Early diagnosis is crucial for reducing and preventing the progression of this condition. However, detecting DR’s early stage...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10820183/ |
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| author | Santosh Kumar Henge Nikhil Reddy Viraati Musaed Alhussein Ajay Shriram Kushwaha Khursheed Aurangzeb Ravleen Singh |
| author_facet | Santosh Kumar Henge Nikhil Reddy Viraati Musaed Alhussein Ajay Shriram Kushwaha Khursheed Aurangzeb Ravleen Singh |
| author_sort | Santosh Kumar Henge |
| collection | DOAJ |
| description | Diabetic retinopathy (DR) is a severe complication of diabetes that affects the retinal structures and can lead to significant visual impairment or even blindness. Early diagnosis is crucial for reducing and preventing the progression of this condition. However, detecting DR’s early stages remains challenging due to subtle symptoms that are difficult to recognize independently. Our proposed model leverages 172 weighted layers to analyze both sequential and non-sequential fundus images for effective DR detection. By incorporating a multi-layered transfer learning approach, 86 layers are used for processing color fundus images, while the remaining 86 layers focus on grayscale images. The model undergoes thorough pre-processing and testing phases, utilizing eight layers of convolutions at each stage to handle various data matrices and integrate global and specialized features. The chi-square testing mechanism refines the evaluation of test cases, contributing to the model’s overall performance. Using multi-decision hybrid techniques, the model achieves a detection accuracy of 98.1%, outperforming other existing models. |
| format | Article |
| id | doaj-art-c39dab185f7a4cf394a17578145fa01b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c39dab185f7a4cf394a17578145fa01b2025-01-21T00:02:24ZengIEEEIEEE Access2169-35362025-01-01138988900510.1109/ACCESS.2024.352515410820183Detection of Diabetic Retinopathy Using a Multi-Decision Inception-ResNet-Blended Hybrid ModelSantosh Kumar Henge0Nikhil Reddy Viraati1Musaed Alhussein2https://orcid.org/0000-0002-5538-6778Ajay Shriram Kushwaha3https://orcid.org/0000-0003-0392-5546Khursheed Aurangzeb4https://orcid.org/0000-0003-3647-8578Ravleen Singh5Department of Computer Science and Engineering, School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, IndiaDepartment of Data Insights, Crisp Shared Services, Columbia, MD, USADepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science and Application, Sharda School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, IndiaDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, IndiaDiabetic retinopathy (DR) is a severe complication of diabetes that affects the retinal structures and can lead to significant visual impairment or even blindness. Early diagnosis is crucial for reducing and preventing the progression of this condition. However, detecting DR’s early stages remains challenging due to subtle symptoms that are difficult to recognize independently. Our proposed model leverages 172 weighted layers to analyze both sequential and non-sequential fundus images for effective DR detection. By incorporating a multi-layered transfer learning approach, 86 layers are used for processing color fundus images, while the remaining 86 layers focus on grayscale images. The model undergoes thorough pre-processing and testing phases, utilizing eight layers of convolutions at each stage to handle various data matrices and integrate global and specialized features. The chi-square testing mechanism refines the evaluation of test cases, contributing to the model’s overall performance. Using multi-decision hybrid techniques, the model achieves a detection accuracy of 98.1%, outperforming other existing models.https://ieeexplore.ieee.org/document/10820183/Adam optimizationclustered classconvolutional neural networkdeep learningdual imagediabetic retinopathy |
| spellingShingle | Santosh Kumar Henge Nikhil Reddy Viraati Musaed Alhussein Ajay Shriram Kushwaha Khursheed Aurangzeb Ravleen Singh Detection of Diabetic Retinopathy Using a Multi-Decision Inception-ResNet-Blended Hybrid Model IEEE Access Adam optimization clustered class convolutional neural network deep learning dual image diabetic retinopathy |
| title | Detection of Diabetic Retinopathy Using a Multi-Decision Inception-ResNet-Blended Hybrid Model |
| title_full | Detection of Diabetic Retinopathy Using a Multi-Decision Inception-ResNet-Blended Hybrid Model |
| title_fullStr | Detection of Diabetic Retinopathy Using a Multi-Decision Inception-ResNet-Blended Hybrid Model |
| title_full_unstemmed | Detection of Diabetic Retinopathy Using a Multi-Decision Inception-ResNet-Blended Hybrid Model |
| title_short | Detection of Diabetic Retinopathy Using a Multi-Decision Inception-ResNet-Blended Hybrid Model |
| title_sort | detection of diabetic retinopathy using a multi decision inception resnet blended hybrid model |
| topic | Adam optimization clustered class convolutional neural network deep learning dual image diabetic retinopathy |
| url | https://ieeexplore.ieee.org/document/10820183/ |
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