Skin Cancer Diagnostic using Machine Learning Techniques – Stationary Wavelet Transform and Random Forest Classifier
S. Mohan Kumar1, J. Ram Kumar2, K. Gopalakrishnan3
1Dr. S. Mohan Kumar*, Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, India.
2Dr. J. Ram Kumar, Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, India.
3Dr. K. Gopalakrishnan, New Horizon College of Engineering, Bangalore, Karnataka, India.
Manuscript received on November 11, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 4705-4708 | Volume-9 Issue-2, December 2019. | Retrieval Number: B9016129219/2019©BEIESP | DOI: 10.35940/ijitee.B9016.129219
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© 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: The abnormal growth of skin cells in the skin is known as skin cancer. It develops in skin due to exposure of the sun. Skin cancer spreads into other parts of the body easily. The death rate of skin cancer is increasing day by day, so, early diagnosis of skin cancer is necessary. In this paper, an efficient method for skin image classification using Stationary Wavelet Transform (SWT) based entropy features and Random Forest (RF) classifier is presented. The input skin images are decomposed by SWT. The skin image features are extracted by the entropy of decomposed skin images and classified using RF classifier. The performance of the system is evaluated in terms of accuracy, sensitivity and specificity. The results show the better classification accuracy of 91.5% at the 3rd level of SWT decomposition based on entropy features and RF classifier, and also their sensitivity and specificity are 90 % and 93 %..
Keywords: Skin Cancer Images, SWT, Entropy Features, RF Classifier
Scope of the Article: Classification