Review on skin cancer detection using AI
Keywords:
air pollution, artificial intelligence, cancer detection, skin, UV radiationAbstract
Skin cancer is caused by reasons such as unhealthy life style, air pollution, UV radiation, etc. Various automated machine learning algorithmic solutions have been created in prevalent years to be used to detect such cancers before any major aggravation has taken place. In this paper there is a review on ways can detect disease and alert us before something becomes serious. The goal of this study is to look at how Artificial Intelligence can be used to diagnose skin cancer. With the use of Artificial Intelligence, people can learn what skin illness they have and what safeguards and steps they should take at an early stage, allowing them to treat the disease successfully. Machine learning will be utilized to determine the ailment and assist us in detecting the outcome. Support vector machine is the most prevalently used classification techniques. The findings of this study will aid doctors in treating disease at its onset, preventing future deterioration.
Downloads
References
Miller DD, Brown EW. Artificial intelligence in medical practice: the question to the answer? Am J Med. (2018)
Kulkarni S, Seneviratne N, Baig MS, Khan AHA. Artificial intelligence in medicine: where are we now?" Acad Radiol. (2019)
Prayer F, Röhrich S, Pan J, Hofmanninger J, Langs G, Prosch HJD, et al. Artificial intelligence in lung imaging. Radiologe. (2019)
Petrone JJNB. FDA approves stroke-detecting AI software. Nat Biotechnol. (2018)
McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. (2020)
Shrivastava VK, Londhe ND, Sonawane RS, Suri JS. A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification. Comput Methods Prog Biomed. (2017)
Han SS, Park GH, LimW, KimMS, ImNa J, Park I, et al.Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: automatic construction of onychomycosis datasets by region-based convolutional deep neural network. PLoS ONE. (2018)
Hogarty DT, Su JC, Phan K, Attia M, Hossny M, Nahavandi S, et al. Artificial intelligence in dermatology—where we are and the way to the future: a review. Am J Clin Dermatol. (2019)
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. (2017)
Brinker TJ, Hekler A, Enk AH, Berking C, Haferkamp S, Hauschild A, et al. Deep neural networks are superior to dermatologists in melanoma image classification. Eur J Cancer. (2019)
Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, et al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J Cancer. (2019)
Lau AY, Staccini P. Artificial intelligence in health: new opportunities, challenges, and practical implications. Yearb Med Inform. (2019)
Cath C. Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Phil Trans R Soc A. (2018)
Argenziano et al., 2012
Miller & Mihm, 2006
Rigel et al., 2010, September
Henning et al., 2007
Vestergaard et al., 2008
H. A. Daanen and F. B. Ter Haar, “3d whole body scanners revisited,” Displays, 2013.
T. Ching, D. S. Himmelstein, B. K. Beaulieu-Jones, A. A. Kalinin, B. T. Do, G. P. Way, E. Ferrero, P.-M. Agapow, M. Zietz, M. M. Hoffman et al., “Opportunities and obstacles for deep learning in biology and medicine,” Journal of The Royal Society Interface, 2018.
Y. Bengio, I. Goodfellow, and A. Courville, Deep learning. Citeseer, 2017
Ge et al., 2017
Narayanamurthy et al., 2018
Ries et al., 2006
Zhang et al., 2020
Siegel et al., 2020
Smith and MacNeil, 2011
Friedman et al., 1985
Al-Masni et al. 2020; Jones et al., 2019
Hasan et al., 2020
Mishraa and Celebi, 2016
Rehman, M.; Khan, S.H.; Danish Rizvi, S.M.; Abbas, Z.; Zafar, A. Classification of Skin Lesion by Interference of Segmentation and Convolotion Neural Network. In Proceedings of the 2018 2nd International Conference on Engineering Innovation (ICEI), Bangkok, Thailand, 5–6 July 2018; pp. 81–85. [CrossRef]
Harley, A.W. An Interactive Node-Link Visualization of Convolutional Neural Networks. In Advances in Visual Computing; Lecture Notes in Computer Science; Springer International Publishing: Cham, Switzerland, 2015; Volume 9474, pp. 867–877. [CrossRef]
Zeiler, M.D.; Fergus, R. Visualizing and Understanding Convolutional Networks. arXiv 2013, arXiv:1311.2901. Available online: http://arxiv.org/abs/1311.2901
Nasr-Esfahani, E.; Samavi, S.; Karimi, N.; Soroushmehr, S.M.R.; Jafari, M.H.; Ward, K.; Najarian, K. Melanoma Detection by Analysis of Clinical Images Using Convolutional Neural Network. In Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 16–20 August 2016; p. 1373. [CrossRef]
Yu, L.; Chen, H.; Dou, Q.; Qin, J.; Heng, P.-A. Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks. IEEE Trans. Med. Imaging 2017, 36, 994–1004. [CrossRef]
DeVries, T.; Ramachandram, D. Skin Lesion Classification Using Deep Multi-Scale Convolutional Neural Networks. arXiv 2017, arXiv:1703.01402. Available online: http://arxiv.org/abs/1703.01402 (accessed on 13 February 2021).
Gonog, L.; Zhou, Y. A Review: Generative Adversarial Networks. In Proceedings of the 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), Xi’an, China, 20 June 2019; pp. 505–510. [CrossRef]
Goodfellow, I. NIPS 2016 Tutorial: Generative Adversarial Networks. arXiv 2017, arXiv:1701.00160. Available online: http: //arxiv.org/abs/1701.00160
Published
How to Cite
Issue
Section
Copyright (c) 2022 International journal of health sciences

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the International Journal of Health Sciences (IJHS) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJHS right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.
Articles published in IJHS can be copied, communicated and shared in their published form for non-commercial purposes provided full attribution is given to the author and the journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
This copyright notice applies to articles published in IJHS volumes 4 onwards. Please read about the copyright notices for previous volumes under Journal History.








