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Using Local Binary Patterns and Convolutional Neural Networks for Melanoma Detection

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Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1038))

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Abstract

Skin cancer is an abnormal growth of skin cells on body parts which get more exposure to sunlight. Detection of cancer in early stages improves patient outcomes, however, manual assessment of medical cells and microscopy images is laborious work, and the results are often subjective so that the agreement between viewers can be low. In this paper, a new method is proposed to detect skin cancer signs such as asymmetry, border, colour and diameter using segmentation and region analysis. Melanoma and non-melanoma skin cancer images have been classified using region analysis, boundary, colour and size measurements. To achieve accurate and computationally efficient results, Local Binary Pattern Convolutional Neural Networks are employed. The proposed method has provided a high classification performance, achieving 0.95 accuracy rate, 0.95 sensitivity, and 0.96 specificity on the ISIC public data sets.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, Univeristy of Central Punjab, Lahore, Pakistan

    Saeed Iqbal & Adnan N. Qureshi

  2. School of Computer Science and Technology, University of Bedfordshire, Luton, UK

    Mukti Akter

Authors
  1. Saeed Iqbal
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  2. Adnan N. Qureshi
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  3. Mukti Akter
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Corresponding author

Correspondence to Saeed Iqbal .

Editor information

Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Iqbal, S., Qureshi, A.N., Akter, M. (2020). Using Local Binary Patterns and Convolutional Neural Networks for Melanoma Detection. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_58

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