A Systematic Analysis of Skin Cancer Detection Using Machine Learning and Deep Learning Techniques
Clicks: 1
ID: 313129
2023
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #595 of 705 articles by views in Journal of Computing & Biomedical Informatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
The skin serves as the primary line for protection against oxidative damage by UV rays on the outside of the body. Skin cancer is now the most often reported malignancy in the world, placing a great effect on economy as well as public health. The most frequent kind of cancer in Caucasians, encompassing both melanoma and non-melanoma, is skin cancer. In this proposed taxonomy, a complete overview of recent developments relevant to cancer diagnosis was discussed. Moreover, a review of melanoma cancer detection methods using machine learning and deep learning with image processing techniques was conducted. It also includes comparison of performance metrics, results, and publicly accessible datasets as well. Conclusion drawn from this study was that deep learning, CNN based models provide more accurate results across variety of datasets among all other machine learning and deep learning models and this also leads directions to the other researchers that which areas will be covered in future.
| Reference Key |
imported_1777059295_69ebc5df047b6
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Farah Sultan, Muhammad Kaleem, Nisar Ahmad, Salman Rashid, Muhammad Azhar Mushtaq, Naveed Ahmad |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2023 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.