Machine Learning Algorithms based Skin Disease Detection
Shuchi Bhadula1, Sachin Sharma2, Piyush Juyal3, Chitransh Kulshrestha4
1Shuchi Bhadula*, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
2Sachin Sharma, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
3Piyush Juyal, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
4Chitransh Kulshrestha, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
Manuscript received on November 12, 2019. | Revised Manuscript received on 24 November, 2019. | Manuscript published on December 10, 2019. | PP: 4044-4049 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7686129219/2019©BEIESP | DOI: 10.35940/ijitee.B7686.129219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Skin disease recognition and observing is a major challenge looked by the medical industry. Because of expanding contamination and utilization of lousy nourishment, the tally of patients experiencing skin related issues is expanding at a quicker rate. Well-being isn’t the main concern, however unfortunate skin hurts our certainty. Customary and appropriate skin checking is a significant advance towards early discovery of any destructive or starting changes in skin that may bring about skin disease. Machine learning methods can add to the improvement of capable frameworks which can order various classes of skin illnesses. To identify skin maladies, first, it is required to separate the skin and non-skin. In this paper, five diverse machine learning algorithms have been chosen and executed on skin infection data set to anticipate the exact class of skin disease. Out of a few machine learning algorithms, we have worked on Random forest, naive bayes, logistic regression, kernel SVM and CNN. A similar examination dependent on confusion matrix parameters and training accuracy has been performed and delineated utilizing graphs. It is discovered that CNN is giving best training precision for the right expectation of skin diseases among all selected.
Keywords: Machine learning, Random forest, naive Bayes, logistic regression, kernel SVM and CNN
Scope of the Article: Machine learning