A Technique for Retinal Detachment Detection Manipulating YOLOv8 Models
A retinal detachment is a serious condition resulting in the retina detaching from its support layers, which are just beneath it. If untreated, this can cause blindness. To detect the classes of retinal detachment in various images, this paper introduces a novel method employing deep learning techn...
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| Language: | English |
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Northern Technical University
2025-03-01
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| Series: | NTU Journal of Engineering and Technology |
| Online Access: | https://journals.ntu.edu.iq/index.php/NTU-JET/article/view/1026 |
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| author | Younis Bashar Younis Fadwa Al Azzo |
| author_facet | Younis Bashar Younis Fadwa Al Azzo |
| author_sort | Younis Bashar Younis |
| collection | DOAJ |
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A retinal detachment is a serious condition resulting in the retina detaching from its support layers, which are just beneath it. If untreated, this can cause blindness. To detect the classes of retinal detachment in various images, this paper introduces a novel method employing deep learning techniques involving the YOLOv8 algorithm. Notably, this marks the first use of the YOLOv8 model for retinal detachment detection. Retinal detachment can be identified with high precision using images obtained from Optical Coherence Tomography (OCT). The proposed work assesses the performance of these models using metrics such as mAP50, recall, and precision by training five YOLOv8 models: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. Among these, the YOLOv8s model had the best performance with a mAP50 of 0.985, a recall of 0.97, and a precision of 0.968. The other models had the following mAP scores: YOLOv8n (0.949), YOLOv8m (0.906), YOLOv8l (0.889), and YOLOv8x (0.907). This demonstrates that the proposed system works effectively in detecting retinal detachment, resulting in highly accurate results mined from complex medical data sets and imaging, thereby making it an important tool in medicine.
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| format | Article |
| id | doaj-art-30aab63158eb49daa3f1fc67eca3c57c |
| institution | Kabale University |
| issn | 2788-9971 2788-998X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Northern Technical University |
| record_format | Article |
| series | NTU Journal of Engineering and Technology |
| spelling | doaj-art-30aab63158eb49daa3f1fc67eca3c57c2025-08-24T13:19:21ZengNorthern Technical UniversityNTU Journal of Engineering and Technology2788-99712788-998X2025-03-014110.56286/rxck4y171027A Technique for Retinal Detachment Detection Manipulating YOLOv8 ModelsYounis Bashar Younis0Fadwa Al Azzo1https://orcid.org/0000-0002-9588-3912Northern Technical UniversityNorthern Technical University A retinal detachment is a serious condition resulting in the retina detaching from its support layers, which are just beneath it. If untreated, this can cause blindness. To detect the classes of retinal detachment in various images, this paper introduces a novel method employing deep learning techniques involving the YOLOv8 algorithm. Notably, this marks the first use of the YOLOv8 model for retinal detachment detection. Retinal detachment can be identified with high precision using images obtained from Optical Coherence Tomography (OCT). The proposed work assesses the performance of these models using metrics such as mAP50, recall, and precision by training five YOLOv8 models: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. Among these, the YOLOv8s model had the best performance with a mAP50 of 0.985, a recall of 0.97, and a precision of 0.968. The other models had the following mAP scores: YOLOv8n (0.949), YOLOv8m (0.906), YOLOv8l (0.889), and YOLOv8x (0.907). This demonstrates that the proposed system works effectively in detecting retinal detachment, resulting in highly accurate results mined from complex medical data sets and imaging, thereby making it an important tool in medicine. https://journals.ntu.edu.iq/index.php/NTU-JET/article/view/1026 |
| spellingShingle | Younis Bashar Younis Fadwa Al Azzo A Technique for Retinal Detachment Detection Manipulating YOLOv8 Models NTU Journal of Engineering and Technology |
| title | A Technique for Retinal Detachment Detection Manipulating YOLOv8 Models |
| title_full | A Technique for Retinal Detachment Detection Manipulating YOLOv8 Models |
| title_fullStr | A Technique for Retinal Detachment Detection Manipulating YOLOv8 Models |
| title_full_unstemmed | A Technique for Retinal Detachment Detection Manipulating YOLOv8 Models |
| title_short | A Technique for Retinal Detachment Detection Manipulating YOLOv8 Models |
| title_sort | technique for retinal detachment detection manipulating yolov8 models |
| url | https://journals.ntu.edu.iq/index.php/NTU-JET/article/view/1026 |
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