دورية أكاديمية

Algorithm of automatic identification of diabetic retinopathy foci based on ultra-widefield scanning laser ophthalmoscopy

التفاصيل البيبلوغرافية
العنوان: Algorithm of automatic identification of diabetic retinopathy foci based on ultra-widefield scanning laser ophthalmoscopy
المؤلفون: Jie Wang, Su-Zhen Wang, Xiao-Lin Qin, Meng Chen, Heng-Ming Zhang, Xin Liu, Meng-Jun Xiang, Jian-Bin Hu, Hai-Yu Huang, Chang-Jun Lan
المصدر: International Journal of Ophthalmology, Vol 17, Iss 4, Pp 610-615 (2024)
بيانات النشر: Press of International Journal of Ophthalmology (IJO PRESS), 2024.
سنة النشر: 2024
المجموعة: LCC:Ophthalmology
مصطلحات موضوعية: diabetic retinopathy, ultra-widefield scanning laser ophthalmoscopy, intelligent diagnosis system, Ophthalmology, RE1-994
الوصف: AIM: To propose an algorithm for automatic detection of diabetic retinopathy (DR) lesions based on ultra-widefield scanning laser ophthalmoscopy (SLO). METHODS: The algorithm utilized the FasterRCNN (Faster Regions with CNN features)+ResNet50 (Residua Network 50)+FPN (Feature Pyramid Networks) method for detecting hemorrhagic spots, cotton wool spots, exudates, and microaneurysms in DR ultra-widefield SLO. Subimage segmentation combined with a deeper residual network FasterRCNN+ResNet50 was employed for feature extraction to enhance intelligent learning rate. Feature fusion was carried out by the feature pyramid network FPN, which significantly improved lesion detection rates in SLO fundus images. RESULTS: By analyzing 1076 ultra-widefield SLO images provided by our hospital, with a resolution of 2600×2048 dpi, the accuracy rates for hemorrhagic spots, cotton wool spots, exudates, and microaneurysms were found to be 87.23%, 83.57%, 86.75%, and 54.94%, respectively. CONCLUSION: The proposed algorithm demonstrates intelligent detection of DR lesions in ultra-widefield SLO, providing significant advantages over traditional fundus color imaging intelligent diagnosis algorithms.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2222-3959
2227-4898
Relation: http://ies.ijo.cn/en_publish/2024/4/20240402.pdf; https://doaj.org/toc/2222-3959; https://doaj.org/toc/2227-4898
DOI: 10.18240/ijo.2024.04.02
URL الوصول: https://doaj.org/article/1504080d6b054abda31aad94ee44c5bd
رقم الأكسشن: edsdoj.1504080d6b054abda31aad94ee44c5bd
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:22223959
22274898
DOI:10.18240/ijo.2024.04.02