CSR U-Net: A Novel Approach for Enhanced Skin Cancer Lesion Image Segmentation
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9521-9_11
Reference12 articles.
1. SM J, P M, Aravindan C, Appavu R (2023) Classification of skin cancer from dermoscopic images using deep neural network architectures. Multimedia Tools Appl 82(10):15763–15778
2. Alenezi F, Armghan A, Polat K (2023) A novel multi-task learning network based on melanoma segmentation and classification with skin lesion images. Diagnostics 13(2):262
3. Dimililer K, Sekeroglu B (2023) Skin lesion classification using CNN-based transfer learning model. Gazi Univ J Sci 36(2):660–673
4. Tschandl P, Rosendahl C, Kittler H (2018) The HAM10000 dataset, a large collection of multisource dermatoscopic images of common pigmented skin lesions. Scientific Data 5:180161
5. Thaajwer MA, Ishanka UP (2020) Melanoma skin cancer detection using image processing and machine learning techniques. In: 2020 2nd international conference on advancements in computing (ICAC). IEEE, Malabe, pp 363–368
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