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Mathematical Modeling of Sensitivity and Specificity for Basal Cell Carcinoma (BCC) Images

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Information and Decision Sciences

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 701))

Abstract

In this paper, mathematical modeling for malignant and non-malignant basal cell carcinoma is proposed. Image features are used for the modeling of growth rate, sensitivity, and specificity. Newton’s law and Hooke’s law play very important role in the modeling of growth, sensitivity, and specificity of basal carcinoma cell (BBC). Two features mean and entropy are used for two different positions. These two features are taken from the image data database of 550. Maximum growth rate of non-malignant and malignant BBC for the two different positions are 7.945, 10 and 19.76, 12, respectively. Maximum sensitivity and specificity calculated for malignant and non-malignant images are 0.8425, 0.3225 and 0.8512, 0.1992, respectively.

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Authors and Affiliations

  1. Department of Electrical Engineering, Gautam Buddha University, Greater Noida, India

    Sudhakar Singh & Shabana Urooj

Authors
  1. Sudhakar Singh
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  2. Shabana Urooj
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Corresponding author

Correspondence to Sudhakar Singh .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Departamento de Engenharia Mecânica, Universidade do Porto, Porto, Portugal

    Joao Manuel R.S. Tavares

  3. Department of Electronics and Communication Engineering, SRMGPC, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  4. School of Computer Application, KIIT University, Bhubaneswar, Odisha, India

    J. R. Mohanty

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Singh, S., Urooj, S. (2018). Mathematical Modeling of Sensitivity and Specificity for Basal Cell Carcinoma (BCC) Images. In: Satapathy, S., Tavares, J., Bhateja, V., Mohanty, J. (eds) Information and Decision Sciences. Advances in Intelligent Systems and Computing, vol 701. Springer, Singapore. https://doi.org/10.1007/978-981-10-7563-6_49

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  • DOI: https://doi.org/10.1007/978-981-10-7563-6_49

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7562-9

  • Online ISBN: 978-981-10-7563-6

  • eBook Packages: EngineeringEngineering (R0)

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