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phat 23(1):

Research Article

novel skin cancer Detection based transfer learning with optimization algorithm using Dermatology Dataset

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  • @ARTICLE{10.4108/eetpht.9.4277,
        author={Polasi Sudhakar and Suresh Chandra Satapathy},
        title={novel skin cancer Detection based transfer learning with optimization algorithm using Dermatology Dataset},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={9},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2023},
        month={10},
        keywords={Fractional Gazelle Optimization Algorithm, Fractional Calculus, Gazelle Optimization Algorithm, Convolution Neural Network, Transfer Learning},
        doi={10.4108/eetpht.9.4277}
    }
    
  • Polasi Sudhakar
    Suresh Chandra Satapathy
    Year: 2023
    novel skin cancer Detection based transfer learning with optimization algorithm using Dermatology Dataset
    PHAT
    EAI
    DOI: 10.4108/eetpht.9.4277
Polasi Sudhakar1,*, Suresh Chandra Satapathy1
  • 1: KIIT University
*Contact email: sudhakar.forall@gmail.com

Abstract

Detecting skin cancer at the preliminary stage is a challenging issue, and is of high significance for the affected patients. Here, Fractional Gazelle Optimization AlgorithmConvolutional Neural Network based Transfer Learning with Visual Geometric Group-16 (FGOACNN based TL with VGG-16) is introduced for primary prediction of skin cancer. Initially, input skin data is acquired from the database and it is fed to the data preprocessing. Here, data preprocessing is done by missing value imputation and linear normalization. Once data is preprocessed, the feature selection is done by the proposed FGOA. Here, the proposed FGOA is an integration of Fractional Calculus (FC) and Gazelle Optimization Algorithm (GOA). After that, skin cancer detection is carried out using CNN-based TL with VGG-16, which is trained by the proposed FGOA and it is an integration of FC and GOA. Moreover, the efficiency of the proposed FGOA_ CNN-based TL with VGG-16 is examined based on five various metrics, like accuracy, Positive Predictive Value (PPV), True Positive Rate (TPR), True Negative Rate (TNR), and Negative Predictive Value (NPV) and the outcome of experimentation reveals that the devised work is highly superior and has attained maximal values of metrics is 92.65%, 90.35%, 91.48%, 93.56%, 90.77% respectively.

Keywords
Fractional Gazelle Optimization Algorithm, Fractional Calculus, Gazelle Optimization Algorithm, Convolution Neural Network, Transfer Learning
Received
2023-08-03
Accepted
2023-10-16
Published
2023-10-30
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.9.4277

Copyright © 2023 P. Sudhakar et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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