An optimized boosting framework for skin lesion segmentation and classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17042-w.pdf
Reference37 articles.
1. Abhishek K, Hamarneh G (2021) Matthews correlation coefficient loss for deep convolutional networks: Application to skin lesion segmentation. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), IEEE. https://doi.org/10.1109/ISBI48211.2021.9433782
2. Al-Huda Z, Yao Y, Yao J, Peng B et al (2023) Weakly supervised skin lesion segmentation based on spot-seeds guided optimal regions. IET Image Process 17(1):239–255. https://doi.org/10.1049/ipr2.12631
3. Alahmadi MD (2022) Multiscale attention U-Net for skin lesion segmentation. IEEE Access 10:59145–59154. https://doi.org/10.1109/ACCESS.2022.3179390
4. Alhudhaif A, Almaslukh B, Aseeri AO et al (2023) A novel nonlinear automated multi-class skin lesion detection system using soft-attention based convolutional neural networks. Chaos Solit Fractals 170:113409. https://doi.org/10.1016/j.chaos.2023.113409
5. Alzahrani S, Al-Bander B, Al-Nuaimy W (2022) Attention mechanism guided deep regression model for acne severity grading. Computers 11(3):31. https://doi.org/10.3390/computers11030031
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