Machine Learning-Based Diabetic Retinopathy Detection: Exploring Techniques and Methods
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2550-2_48
Reference19 articles.
1. Arsalan M, Owais M, Mahmood T, Cho SW, Park KR (2019) Aiding the diagnosis of diabetic and hypertensive retinopathy using artificial intelligence-based semantic segmentation. J Clin Med 8(9):1446
2. Dai G, He W, Xu L, Pazo EE, Lin T, Liu S, Zhang C (2020) Exploring the effect of hypertension on retinal microvasculature using deep learning on East Asian population. PLoS ONE 15(3):e0230111
3. Kumar S, Pilania U, Nandal N (2023) A systematic study of artificial intelligence-based methods for detecting brain tumors. Inf Autom 22(3):541–575
4. Şüyun SB, Taşdemir Ş, Biliş S, Milea A (2021) Using a deep learning system that classifies hypertensive retinopathy based on the fundus images of patients of wide age. Traitement du Signal 38(1)
5. Bajwa A, Nosheen N, Talpur KI, Akram S (2023) A prospective study on diabetic retinopathy detection based on modify convolutional neural network using fundus images at Sindh institute of ophthalmology and visual sciences. Diagnostics 13(3):393
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