Multi-class Diabetic Retinopathy Classification Using Transfer Learning and MixUp Data Augmentation
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66850-0_34
Reference22 articles.
1. Chen, X., Wang, X., Zhang, K., et al.: Recent advances and clinical applications of deep learning in medical image analysis. Med. Image Anal. 79, 102444 (2022). https://doi.org/10.1016/j.media.2022.102444
2. Li, X., Li, C., Rahaman, M.M., et al.: A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artif. Intell. Rev. 55, 4809–4878 (2022). https://doi.org/10.1007/s10462-021-10121-0
3. Prabhavathy, P., Tripathy, B.K., Venkatesan, M.: Analysis of diabetic retinopathy detection techniques using CNN models. In: Mishra, S., Tripathy, H.K., Mallick, P., Shaalan, K. (eds.) Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis. SCI, vol. 1024, pp. 87–102. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-1076-0_6
4. Cheung, N., Mitchell, P., Wong, T.Y.: Diabetic retinopathy. Lancet 376, 124–136 (2010). https://doi.org/10.1016/S0140-6736(09)62124-3
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