Journal of Applied Science and Engineering (May 2022)

A Granular Parakeratosis Classification using SVM Hinge and Cross Validation

  • Sheetal Janthakal,
  • Girisha Hosalli

DOI
https://doi.org/10.6180/jase.202301_26(1).0004
Journal volume & issue
Vol. 26, no. 1
pp. 35 – 42

Abstract

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Now-a-days, a challenging task in the medical field is the diagnosis of skin illness considering numerous characteristics such as color, size, and the lesion region. Dermoscopy is a technique that has been frequently used to diagnose skin lesions. Researchers have recently demonstrated a keen interest in building an automated diagnosis system, and a satisfying result can be achieved with a high degree of skill, as skin lesion classification necessitates a great deal of knowledge and expertise. Automated skin lesion classification in dermoscopy images is an essential way to improve diagnostic performance. This paper presents the power of convolutional neural networks in classifying the skin lesions into two different categories, namely Granular Parakeratosis and Paraneoplastic Pemphigus. The proposed method includes implementation of Support Vector Machine with hinge loss and linear activation function for classification of lesions and this output is fed to the 10-fold cross validation model, yielding an accuracy of 94%, sensitivity of 93%, and specificity of 91%. The proposed strategy outperforms the SVM kernel Radial basis function (RBF), which was created specifically for binary classification problems.

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