LCAM-Net: Local Context Attention Network for Diabetic Retinopathy Severity Classification
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62836-8_33
Reference29 articles.
1. Abràmoff, M.D., et al.: Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest. Ophthalmol. Visual Sci. 57(13), 5200–5206 (2016). https://doi.org/10.1167/iovs.16-19964
2. Alahmadi, M.D.: Texture attention network for diabetic retinopathy classification. IEEE Access 10, 55522–55532 (2022). https://doi.org/10.1109/ACCESS.2022.3177651
3. Alyoubi, W.L., Abulkhair, M.F., Shalash, W.M.: Diabetic retinopathy fundus image classification and lesions localization system using deep learning. Sensors 21(11), 3704 (2021). https://doi.org/10.3390/s21113704
4. Bodapati, J.D.: Stacked convolutional auto-encoder representations with spatial attention for efficient diabetic retinopathy diagnosis. Multimed. Tools Appli. 1–24 (2022). https://doi.org/10.1007/s11042-022-12811-5
5. Cheloni, R., Gandolfi, S.A., Signorelli, C., Odone, A.: Global prevalence of diabetic retinopathy: protocol for a systematic review and meta-analysis. BMJ Open 9(3), e022188 (2019). https://doi.org/10.1136/bmjopen-2018-022188
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