Highly Accurate Melanoma Detection from Skin Images using Multiple Feature Extraction and Support Vector Machine (SVM)
Shiji A S1, Helintha Graceline H2, Abisha Jey J B3
1Shiji.A.S*, Department of Electronics and Communication Engineering, Narayanaguru college of Engineering, Manjalumoodu, India.
2Helintha Graceline.H, Department of Electronics and Communication Engineering, Narayanaguru college of Engineering, Manjalumoodu, India.
3Abisha Jey J B, Department of Electronics and Communication Engineering, Narayanaguru college of Engineering, Manjalumoodu India.
Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3315-3322 | Volume-8 Issue-5, January 2020. | Retrieval Number: D9150118419/2020©BEIESP | DOI: 10.35940/ijrte.D9150.018520
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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: Digital image processing (DIP) plays a major role in the biomedical image segmentation and classification process. Melanoma detection from skin images is most widely using the application in biomedical imaging. In this paper, a novel algorithm for Melanoma Detection from digital images using multiple feature extraction with a Support Vector Machine (SVM) for highly accurate classification. Multiple features such as color, texture and statistical features are considered to train the SVM. To increase the classification sensitivity, feature extraction is done in (Red, Green, Blue) RGB and (Hue, Intensity, Saturation) HIS color domains. Two separate modules for training and testing is performed with collected sample data by using medical experts. To separate the region from the skin, a cantor-based segmentation technique is used in the gray level component of the input image. The proposed method is tested with various images that are collected from different patients from different places. Form the result validation it is clear that the proposed algorithm can give maximum accuracy of 95% which is best when compared to the conventional classification algorithms.
Keywords: Melanoma, RGB, HIS, Support Vector Machine, Texture Feature, Statistical Feature.
Scope of the Article: Machine Design.