A Granular Parakeratosis Classification using SVM Hinge and Cross Validation

Sheetal  Janthakal; Girisha  Hosalli

doi:10.6180/jase.202301_26(1).0004

A Granular Parakeratosis Classification using SVM Hinge and Cross Validation

Computer Science and Information Engineering

Sheetal JanthakalThis email address is being protected from spambots. You need JavaScript enabled to view it.¹ and Girisha Hosalli¹

¹Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College, Bellary, Karnataka, India

Received: November 21, 2021
Accepted: January 25, 2022
Publication Date: March 18, 2022

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202301_26(1).0004

ABSTRACT

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.

Keywords: 10-fold Cross Validation, Convolutional Neural Networks, Hinge Loss, Linear Activation Function, Support Vector Machine

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