A Novel Approach Using Transfer Learning Architectural Models Based Deep Learning Techniques for Identification and Classification of Malignant Skin Cancer-Reference-Cited by-同舟云学术

A Novel Approach Using Transfer Learning Architectural Models Based Deep Learning Techniques for Identification and Classification of Malignant Skin Cancer

Published:2024-02 Issue:4 Volume:134 Page:2183-2201
ISSN:0929-6212
Container-title:Wireless Personal Communications
language:en
Short-container-title:Wireless Pers Commun

Author:

Subramanian Balambigai^ORCID,Muthusamy Suresh^ORCID,Thangaraj Kokilavani^ORCID,Panchal Hitesh^ORCID,Kasirajan Elavarasi,Marimuthu Abarna,Ravi Abinaya

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11277-024-11006-5.pdf

Reference53 articles.

1. The skin cancer foundation (2022) https://www.skincancer.org/.

2. Nasr-Esfahani, E., Samavi, S., Karimi, N., Soroushmehr, S. M. R., Jafari, M. H., Ward, K., & Najarian, K. (2016) Melanoma detection by analysis of clinical images using convolutional neural network. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1373–1376).

3. Gessert, N., Sentker, T., Madesta, F., Schmitz, R., Kniep, H., Baltruschat, I., Werner, R., & Schlaefer A. (2018). Skin lesion diagnosis using ensembles, unscaled multi-crop evaluation and loss weighting, arXiv preprint arXiv:1808.01694.

4. Kadampur, M. A., & Al Riyaee, S. (2020). Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images. Informatics in Medicine Unlocked, 18, 100282.

5. Harangi, B., Baran, A., & Hajdu, A., (2018) Classification of skin lesions using an ensemble of deep neural networks. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2575–2578).