The Effect of Image Preprocessing Algorithms on Diabetic Foot Ulcer Classification
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66958-3_25
Reference21 articles.
1. Ahmed, S., Naveed, H.: Bias adjustable activation network for imbalanced data—dabetic foot ulcer challenge 2021. In: Yap, M.H., Cassidy, B., Kendrick, C. (eds.) DFUC 2021. LNCS, vol. 13183, pp. 50–61. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-94907-5_4
2. Alzubaidi, L., et al.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8, 1–74 (2021)
3. Armstrong, D.G., Lavery, L.A., Harkless, L.B.: Validation of a diabetic wound classification system: the contribution of depth, infection, and ischemia to risk of amputation. Diabetes Care 21(5), 855–859 (1998)
4. Bansal, R., Raj, G., Choudhury, T.: Blur image detection using Laplacian operator and open-cv. In: 2016 International Conference System Modeling and Advancement in Research Trends (SMART), pp. 63–67 (2016). https://doi.org/10.1109/SYSMART.2016.7894491
5. Bloch, L., Brüngel, R., Friedrich, C.M.: Boosting EfficientNets ensemble performance via pseudo-labels and synthetic images by pix2pixHD for infection and ischaemia classification in diabetic foot ulcers. In: Yap, M.H., Cassidy, B., Kendrick, C. (eds.) DFUC 2021. LNCS, vol. 13183, pp. 30–49. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-94907-5_3
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