Skin cancer detection and classification based on differential analyzer algorithm
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/s11042-023-14409-x.pdf
Reference76 articles.
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2. Abuzaghleh O, Barkana BD, Faezipour M (2015) Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention. IEEE J Transl Eng Health Med 3:1–12
3. Ahmed Thaajwer MA, Piumi Ishanka UA (2020) Melanoma skin cancer detection using image processing and machine learning techniques. ICAC 2020 - 2nd Int Conf Adv Comput Proc, pp. 363–368. https://doi.org/10.1109/ICAC51239.2020.9357309.
4. Albert BA (2020) Deep learning from limited training data: novel segmentation and ensemble algorithms applied to automatic melanoma diagnosis. IEEE Access 8:31254–31269. https://doi.org/10.1109/ACCESS.2020.2973188
5. Al-masni MA, Al-antari MA, Choi M-T, Han S-M, Kim T-S (2018) Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Comput Methods Prog Biomed 162:221–231. https://doi.org/10.1016/j.cmpb.2018.05.027
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Skin Cancer Image Segmentation Based on Midpoint Analysis Approach;Journal of Imaging Informatics in Medicine;2024-04-16
2. An Innovative Fusion of Deep Learning and the Differential Analyzer Approach (DAA-Deep model) for Enhanced Skin Cancer Detection;2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT);2023-10-20
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