CTNet: convolutional transformer network for diabetic retinopathy classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09304-3.pdf
Reference86 articles.
1. WHO (2021) Update from the seventy-fourth world health assembly, 28 May 2021. https://www.who.int/news/item/28-05-2021-update-from-the-seventy-fourth-world-health-assembly-28-may-2021. Accessed on 28 Feb 2022
2. Raman R, Vasconcelos J, Rajalakshmi R, Prevost A, Ramasamy K, Mohan V, Mohan D, Rani P, Conroy D, Das T, Sivaprasad S (2022) SMART India Study Collaborators. Prevalence of diabetic retinopathy in India stratified by known and undiagnosed diabetes, urban-rural locations, and socioeconomic indices: results from the SMART India population-based cross-sectional screening study. Lancet Glob Health https://doi.org/10.1016/S2214-109X(22)00411-9
3. Bala R, Sharma A, Goel N (2023) Comparative analysis of diabetic retinopathy classification approaches using machine learning and deep learning techniques. Arch Computat Methods Eng. https://doi.org/10.1007/s11831-023-10002-5
4. Thomas R, Halim S, Gurudas S, Sivaprasad S, Owens D (2019) IDF diabetes atlas: a review of studies utilising retinal photography on the global prevalence of diabetes related retinopathy between 2015 and 2018. Diabetes Res Clin Prac 157:107840. https://doi.org/10.1016/j.diabres.2019.107840
5. Huang X, Wang H, Xue W, Xiang S, Huang H, Meng L, Ma G, Ullah A, Zhang G (2020) Study on time-temperature-transformation diagrams of stainless steel using machine-learning approach. Comput Mater Sci 171:109282. https://doi.org/10.1016/j.commatsci.2019.109282
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