Optimal Convolutional Networks for Staging and Detecting of Diabetic Retinopathy
Diabetic retinopathy (DR) is the main ocular complication of diabetes. Asymptomatic for a long time, it is subject to annual screening using dilated fundus or retinal photography to look for early signs. Fundus photography and optical coherence tomography (OCT) are used by ophthalmologists to assess...
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MDPI AG
2025-03-01
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| author | Minyar Sassi Hidri Adel Hidri Suleiman Ali Alsaif Muteeb Alahmari Eman AlShehri |
| author_facet | Minyar Sassi Hidri Adel Hidri Suleiman Ali Alsaif Muteeb Alahmari Eman AlShehri |
| author_sort | Minyar Sassi Hidri |
| collection | DOAJ |
| description | Diabetic retinopathy (DR) is the main ocular complication of diabetes. Asymptomatic for a long time, it is subject to annual screening using dilated fundus or retinal photography to look for early signs. Fundus photography and optical coherence tomography (OCT) are used by ophthalmologists to assess retinal thickness and structure, as well as detect edema, hemorrhage, and scarring. The effectiveness of ConvNet no longer needs to be demonstrated, and its use in the field of imaging has made it possible to overcome many barriers, which were until now insurmountable with old methods. Throughout this study, a robust and optimal deep ConvNet is proposed to analyze fundus images and automatically distinguish between healthy, moderate, and severe DR. The proposed model combines the use of the ConvNet architecture taken from ImageNet, data augmentation, class balancing, and transfer learning in order to establish a benchmarking test. A significant improvement at the level of middle class which corresponds to the early stage of DR, which was the major problem in previous studies. By eliminating the need for retina specialists and broadening access to retinal care, the proposed model is substantially more robust in objectively early staging and detecting DR. |
| format | Article |
| id | doaj-art-b787041def094f11858bed75a47ea3ff |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj-art-b787041def094f11858bed75a47ea3ff2025-08-20T02:11:24ZengMDPI AGInformation2078-24892025-03-0116322110.3390/info16030221Optimal Convolutional Networks for Staging and Detecting of Diabetic RetinopathyMinyar Sassi Hidri0Adel Hidri1Suleiman Ali Alsaif2Muteeb Alahmari3Eman AlShehri4Department of Computer Science, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Science, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Science, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Science, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Science, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDiabetic retinopathy (DR) is the main ocular complication of diabetes. Asymptomatic for a long time, it is subject to annual screening using dilated fundus or retinal photography to look for early signs. Fundus photography and optical coherence tomography (OCT) are used by ophthalmologists to assess retinal thickness and structure, as well as detect edema, hemorrhage, and scarring. The effectiveness of ConvNet no longer needs to be demonstrated, and its use in the field of imaging has made it possible to overcome many barriers, which were until now insurmountable with old methods. Throughout this study, a robust and optimal deep ConvNet is proposed to analyze fundus images and automatically distinguish between healthy, moderate, and severe DR. The proposed model combines the use of the ConvNet architecture taken from ImageNet, data augmentation, class balancing, and transfer learning in order to establish a benchmarking test. A significant improvement at the level of middle class which corresponds to the early stage of DR, which was the major problem in previous studies. By eliminating the need for retina specialists and broadening access to retinal care, the proposed model is substantially more robust in objectively early staging and detecting DR.https://www.mdpi.com/2078-2489/16/3/221diabetic retinopathydeep learningConvNetImageNetSGD |
| spellingShingle | Minyar Sassi Hidri Adel Hidri Suleiman Ali Alsaif Muteeb Alahmari Eman AlShehri Optimal Convolutional Networks for Staging and Detecting of Diabetic Retinopathy Information diabetic retinopathy deep learning ConvNet ImageNet SGD |
| title | Optimal Convolutional Networks for Staging and Detecting of Diabetic Retinopathy |
| title_full | Optimal Convolutional Networks for Staging and Detecting of Diabetic Retinopathy |
| title_fullStr | Optimal Convolutional Networks for Staging and Detecting of Diabetic Retinopathy |
| title_full_unstemmed | Optimal Convolutional Networks for Staging and Detecting of Diabetic Retinopathy |
| title_short | Optimal Convolutional Networks for Staging and Detecting of Diabetic Retinopathy |
| title_sort | optimal convolutional networks for staging and detecting of diabetic retinopathy |
| topic | diabetic retinopathy deep learning ConvNet ImageNet SGD |
| url | https://www.mdpi.com/2078-2489/16/3/221 |
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