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Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers.

Most common and deadly type of cancer is Skin cancer. The destructive kind of cancers in skin is Melanoma as well as it can be identified at the initial stage and can be cured completely. For the diagnosis of melanoma, the identification of the melanocytes in the area of epidermis is an essential st...

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Published in:Journal of Medical Systems Vol. 43; no. 8
Main Authors: Murugan, A., Nair, S.Anu H., Kumar, K. P. Sanal
Format: algorithm equations & formulas pictorial research tables/charts Journal Article
Published: Springer Nature Aug2019
Online Access:View this record in EBSCOhost
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      dt: Aug2019
      vid: 43
      iid: 8
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      pub: Springer Nature
      place: New York, New York
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        137490059
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        10.1007/s10916-019-1400-8
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        atl: Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers.
      aug:
        au:
          Murugan, A.
          Nair, S.Anu H.
          Kumar, K. P. Sanal
        affil: Department of Computer and Information Sciences, Annamalai University, Chidambaram, India
      sug:
        subj:
          Skin Neoplasms Diagnosis
          Skin Neoplasms Radiography
          Image Processing, Computer Assisted Methods
          Melanoma Diagnosis
          Algorithms Utilization
          Human
          Skin Neoplasms Classification
          Computer Simulation
          Machine Learning
          Factor Analysis
          Sensitivity and Specificity
      ab: Most common and deadly type of cancer is Skin cancer. The destructive kind of cancers in skin is Melanoma as well as it can be identified at the initial stage and can be cured completely. For the diagnosis of melanoma, the identification of the melanocytes in the area of epidermis is an essential stage. In this paper the watershed segmentation method is implemented for segmentation. The extracted segments are subjected to feature extraction. The features extracted are shape, ABCD rule and GLCM. The extracted features are then used for classification. The classifiers are kNN (k Nearest Neighbor), Random Forest and SVM (Support Vector Machine). Among different classifiers, the SVM classifier provided better results for the skin lesions classification.
      pubtype: Academic Journal
      doctype:
        algorithm
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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