Identification of Skin Disease Using Machine Learning

DOI

10.2991/978-94-6463-196-8_9How to use a DOI?

Keywords

Skin Disease; K-Means Clustering; SVM; KNN; Color Feature; Texture feature; Haar Feature

Abstract

Skin diseases are a serious health issue that affects a large number of individuals. In recent years, with the fast advancement of technology and the use of various data mining approaches, dermatological predictive classification has become increasingly predictive and accurate. It is more helpful to dermatologists to identify the disease, As a result, the development of machine learning approaches capable of efficiently the purpose of this study is to make an application of identification of skin disease images by using the machine learning method, Support Vector Machine (SVM), and KNN techniques. Early detection of skin diseases is performed using image processing and machine learning. This study aims to determine the classification of skin diseases in humans. Each skin disease has symptoms. It has five skin diseases, such as acne, psoriasis, wrath, psoriasis, and ulcers. We have collected 314 skin disease images from the government hospital in Aurangabad with the help of a mobile camera and a Sony HD camera. A Gaussian filter is used for image pre-processing. The segmentation method is used for K-Means Clustering and the feature extraction method is used for feature extraction. We have used the haar feature, color feature, FCM, OS-FCM, GLCM, and LBF features for classifications. Based on the result, the SVM is given 92% accuracy for haar feature, FCM, and OS-FCM. And the KNN classifier and K-Means are given 89% and 89% accuracy using a mobile phone camera dataset.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Minakshi M. Sonawane
AU  - Ramdas D. Gore
AU  - Bharti W. Gawali
AU  - Ramesh R. Manza
AU  - Sudhir N. Mendhekar
PY  - 2023
DA  - 2023/08/10
TI  - Identification of Skin Disease Using Machine Learning
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 99
EP  - 113
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_9
DO  - 10.2991/978-94-6463-196-8_9
ID  - Sonawane2023
ER  -