Manuscript Number : IJSRSET2292116

Review Paper on Skin Disease Detection Using Machine Learning and Image Processing

Authors(2) :-Miss.Gunjan Chauhan, Dr. S.A. Ubale

Skin illness is the most common sickness on the planet. When diagnosing skin disorders, dermatologists must have a high level of competence and accuracy, so a computer- aided skin disease diagnosis model is presented as a more objective and reliable solution. Many research have been carried out to aid in the diagnosis of skin diseases such skin cancer and tumours. However, due to factors such as low contrast between lesions and skin, visual similarity between the Disease and non- Disease parts, and so on, correct disease recognition is highly difficult. This study aims to detect skin disease from a skin image and analyse it by applying a filter to remove noise and unwanted items, changing the image to grey to aid processing, and extracting useful information. This can be used to indicate emergency preparedness and provide proof of any type of skin disease.

Authors and Affiliations

Miss.Gunjan Chauhan
Department of Computer Engineering, Zeal College of Engineering and Research Pune, Maharashtra, India
Dr. S.A. Ubale
Department of Computer Engineering, Zeal College of Engineering and Research Pune, Maharashtra, India

Deep learning, CNN, facial skin disease, medical image processing

  1. X. Xiaoyun, H. Chaofei, Z.Weiqi, C. Chen, L. Lixia, L. Queping, P. Cong, Z. Shuang, S. Juan, and C. Xiang, “Possible involvement of F1F0-ATP synthase and intracellular ATP in Keratinocyte differentiation in normal skin and skin lesions,” Sci. Rep., vol. 7, Feb. 2017, Art. no. 42672.
  2. A. Bewley, “The neglected psychological aspects of skin disease,” Brit. Med. J., vol. 358, p. 3208, Jul. 2017.
  3. A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, pp. 115 118, Feb. 2017.
  4. X. Zhang, S. Wang, J. Liu, and C. Tao, “Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge,” Med. Inform. Decis. Making, vol. 18, no. 2, p. 59, 2018.
  5. Y. Gurovich, Y. Hanani, O. Bar, G. Nadav, N. Fleischer, D. Gelbman, L. Basel-Salmon, P. M. Krawitz, S. B. Kamphausen, M. Zenker, L. M. Bird, and K.W. Gripp, “Identifying facial phenotypes of genetic disorders using deep learning,” Nature Med., vol. 25, pp. 60 64, Jan. 2019.
  6. A. Melina, N. N. Dinh, B. Tafuri, G. Schipani, S. Nistico`, C. Cosentino, F. Amato, D. Thiboutot, and A. Cherubini, “Artificial intelligence for the objective evaluation of acne investigator global assessment,” J. Drugs Dermatology, vol. 17, no. 9, pp. 1006 1009, 2018.

Publication Details

Published in : Volume 9 | Issue 2 | March-April 2022
Date of Publication : 2022-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 466-471
Manuscript Number : IJSRSET2292116
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Miss.Gunjan Chauhan, Dr. S.A. Ubale, " Review Paper on Skin Disease Detection Using Machine Learning and Image Processing, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 2, pp.466-471, March-April-2022. Journal URL : https://res.ijsrset.com/IJSRSET2292116

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