A NEW DEEP LEARNING MODEL FOR SKIN CANCER CLASSIFICATION

  • Unique Paper ID: 167641
  • Volume: 11
  • Issue: 4
  • PageNo: 130-134
  • Abstract:
  • Skin cancer is one of the top three perilous types of cancer caused by damaged DNA that can cause death. This damaged DNA begins cells to grow uncontrollably and nowadays it is getting increased speedily. There exist some researches for the computerized analysis of malignancy in skin lesion images. However, analysis of these images is very challenging having some troublesome factors like light reflections from the skin surface, variations in color illumination, different shapes, and sizes of the lesions. As a result, evidential automatic recognition of skin cancer is valuable to build up the accuracy and proficiency of pathologists in the early stages.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{167641,
        author = {A SRIJA and B. MURALI},
        title = {A NEW DEEP LEARNING MODEL FOR SKIN CANCER CLASSIFICATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {4},
        pages = {130-134},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167641},
        abstract = {Skin cancer is one of the top three perilous types of cancer caused by damaged DNA that can cause death. This damaged DNA begins cells to grow uncontrollably and nowadays it is getting increased speedily. There exist some researches for the computerized analysis of malignancy in skin lesion images. However, analysis of these images is very challenging having some troublesome factors like light reflections from the skin surface, variations in color illumination, different shapes, and sizes of the lesions. As a result, evidential automatic recognition of skin cancer is valuable to build up the accuracy and proficiency of pathologists in the early stages.},
        keywords = {Skin Cancer, Damaged DNA, Computerized Analysis, Image Analysis Challenges, Automatic Recognition},
        month = {September},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 4
  • PageNo: 130-134

A NEW DEEP LEARNING MODEL FOR SKIN CANCER CLASSIFICATION

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