Improving Automated Diagnosis of Diabetic Retinopathy: Exploring the Influence of Segmented Retinal Blood Vessel Images Through Deep Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1961-7_36
Reference15 articles.
1. de Sousa TF, Camilo CG (2023) HDeep: hierarchical deep learning combination for detection of diabetic retinopathy. Procedia Comput Sci 222:425–434
2. Zang P, Hormel TT, Hwang TS, Bailey ST, Huang D, Jia Y (2023) Deep-learning–aided diagnosis of diabetic retinopathy, age-related macular degeneration, and glaucoma based on structural and angiographic OCT. Ophthalmol Sci 3(1):100245
3. Patil SA, Patil MS, Giraddi S, Chickerur S, Boormane VM, Gamanagatti G (2022) Pipeline parallelism in distributed deep learning for diabetic retinopathy classification. Procedia Comput Sci 215:393–402
4. Athira TR, Nair JJ (2022) Diabetic retinopathy grading from color fundus images: an autotuned deep learning approach. Procedia Comput Sci 218(2022):1055–1066
5. Goswami A, Sharma D, Mathuku H, Gangadharan SMP, Yadav CS (2022) Change detection in remote sensing image data comparing algebraic and machine learning methods. Electronics Article id: 1505208
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