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Clustering-Based Melanoma Detection in Dermoscopy Images Using ABCD Parameters

  • J. Jacinth PoornimaEmail author
  • J. Anitha
  • H. Asha Gnana Priya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 766)

Abstract

Melanoma, dangerous among skin cancer, becomes fatal when not diagnosed and treated at the earliest. It can be correctly predicted only by the expert dermatologists. Owing to lack of experts, computer-aided diagnosis is preferred nowadays. Here we have proposed the image processing algorithm based on clustering to identify melanoma. A total of 170 images taken from the standard database are used to test the algorithm. Various filters and pre-processing techniques have been analyzed for better skin enhancement. The lesion portion is segmented using K-means clustering algorithm. Then the features are extracted from the segmented lesion, and total dermatoscopy score was calculated. This score was calculated for all images and are classified into melanoma and non-melanoma. Finally, the classification accuracy of the algorithm is computed.

Keywords

Skin cancer Dermatoscopic image Melanoma K-means clustering ABCD features 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • J. Jacinth Poornima
    • 1
    Email author
  • J. Anitha
    • 1
  • H. Asha Gnana Priya
    • 1
  1. 1.Karunya Institute of Technology and SciencesCoimbatoreIndia

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