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Performance analysis of melanoma classifier using electrical modeling technique

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Abstract

An efficient and novel modeling approach is proposed in this paper for identifying proteins or genes involved in melanoma skin cancer. Two types of classifiers are modeled, based on the chemical structure and hydropathy property of amino acids. These classifiers are further implemented using NI LabVIEW–based hardware kit to observe the real-time response for proper diagnosis. The phase responses, pole-zero diagrams, and transient responses are examined to screen out the genes related to melanoma from healthy genes. The performance of the proposed classifier is measured using various performance measurement metrics in terms of accuracy, sensitivity, specificity, etc. The classifier is experimented along with a color code scheme on skin genes and illustrates the superiority in comparison with traditional methods by achieving 94% of classification accuracy with 96% of sensitivity.

Graphical abstract

An equivalent electrical model is developed for designing melanoma classifier. Initially, each amino acid is modeled using the RC passive circuit depending on their physicochemical structure and hydropathy nature, to form a gene structure model. The melanoma-related genes are detected by phase, transient, and color code analysis.

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Acknowledgments

The working facility is provided by the University of Engineering and Management, Kolkata-700156.

Funding

The authors would like to thank DST, Science and Engineering Research Board (EEQ/2017/000293), Govt. of India, for funding the research work.

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  1. Department of Electrical and Electronics Engineering, University of Engineering and Management, Kolkata, 700135, India

    Tanusree Roy & Pranabesh Bhattacharjee

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  1. Tanusree Roy
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Correspondence to Tanusree Roy.

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Roy, T., Bhattacharjee, P. Performance analysis of melanoma classifier using electrical modeling technique. Med Biol Eng Comput 58, 2443–2454 (2020). https://doi.org/10.1007/s11517-020-02241-6

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  • DOI: https://doi.org/10.1007/s11517-020-02241-6

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