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Non-Invasive Method of Melanoma Detection on the Skin Surface through Extraction of Image Features Using Modified CAT Optimization Algorithm


Affiliations
1 Department of Electronics and Communication, Aalim Muhammed Salegh College of Engineering, I.A.F., Avadi, Chennai 600 055, India
 

In this study, melanoma was detected at an early stage using modified CAT optimization algorithm (MCOA) based on non-convex boundary edge extraction, pixel size, shape and intensity variations on the skin. MCOA can detect skin cancer at an early stage by extracting the non-convex border of the affected region prevent cancer spread. Thus melanoma is curable when detected at an early stage. MCOA extracts image features and obtains non-convex boundaries of melanoma in the skin image. The non-convex boundary region leads to visualization of discriminative features of melanoma based on the region of interest and scaling. The proposed MCOA delineates the affected region through non-convex border extraction and edge detection. An accuracy of 85% was obtained in the detection of melanoma using MCOA, when compared to traditional algorithms.

Keywords

Contour Refinement, Edge Detection, Melanoma, Non-Convex Boundary, Optimization Algorithm.
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  • Non-Invasive Method of Melanoma Detection on the Skin Surface through Extraction of Image Features Using Modified CAT Optimization Algorithm

Abstract Views: 260  |  PDF Views: 112

Authors

N. Prabhakaran
Department of Electronics and Communication, Aalim Muhammed Salegh College of Engineering, I.A.F., Avadi, Chennai 600 055, India

Abstract


In this study, melanoma was detected at an early stage using modified CAT optimization algorithm (MCOA) based on non-convex boundary edge extraction, pixel size, shape and intensity variations on the skin. MCOA can detect skin cancer at an early stage by extracting the non-convex border of the affected region prevent cancer spread. Thus melanoma is curable when detected at an early stage. MCOA extracts image features and obtains non-convex boundaries of melanoma in the skin image. The non-convex boundary region leads to visualization of discriminative features of melanoma based on the region of interest and scaling. The proposed MCOA delineates the affected region through non-convex border extraction and edge detection. An accuracy of 85% was obtained in the detection of melanoma using MCOA, when compared to traditional algorithms.

Keywords


Contour Refinement, Edge Detection, Melanoma, Non-Convex Boundary, Optimization Algorithm.

References





DOI: https://doi.org/10.18520/cs%2Fv124%2Fi5%2F562-569