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The skin cancer classification using deep convolutional neural network

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Abstract

This paper addresses the demand for an intelligent and rapid classification system of skin cancer using contemporary highly-efficient deep convolutional neural network. In this paper, we mainly focus on the task of classifying the skin cancer using ECOC SVM, and deep convolutional neural network. RGB images of the skin cancers are collected from the Internet. Some collected images have noises such as other organs, and tools. These images are cropped to reduce the noise for better results. In this paper, an existing, and pre-trained AlexNet convolutional neural network model is used in extracting features. A ECOC SVM clasifier is utilized in classification the skin cancer. The results are obtained by executing a proposed algorithm with a total of 3753 images, which include four kinds of skin cancers images. The implementation result shows that maximum values of the average accuracy, sensitivity, and specificity are 95.1 (squamous cell carcinoma), 98.9 (actinic keratosis), 94.17 (squamous cell carcinoma), respectively. Minimum values of the average in these measures are 91.8 (basal cell carcinoma), 96.9 (Squamous cell carcinoma), and 90.74 (melanoma), respectively.

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Acknowledgements

In this research, Ulzii-Orshikh Dorj, Malrey Lee, and Keun-Kwang Lee conceived and designed the experiments; Ulzii-Orshikh Dorj, Keun-Kwang Lee performed the experiments; Ulzii-Orshikh Dorj, Keun-Kwang Lee, and Jae-Young Choi collected and analyzed the data; Ulzii-Orshikh Dorj, Keun-Kwang Lee contributed materials / analysis tools; Ulzii-Orshikh Dorj, Malrey Lee, Keun-Kwang Lee and Jae-Young Choi wrote the paper.

All named authors hereby declare that they have no conflicts of interest to disclose.

This work is supported by the National Research Foundation of Korea (NRF) granted by the Korea government (MSP) (No: 2017R1A2B4006667).

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Authors and Affiliations

  1. Center for Advanced Image and Information Technology, School of Electronics & Information Engineering, Chon Buk National University, 664-14, 1Ga, Deokjin-Dong, Jeonju, Chon Buk, 561-756, South Korea

    Ulzii-Orshikh Dorj & Malrey Lee

  2. Department of Skin and Beauty Arts, Koguryeo College, Naju, 58280, South Korea

    Keun-Kwang Lee

  3. Department of Computer Engineering, Sungkyunkwan University, Seoul, South Korea

    Jae-Young Choi

Authors
  1. Ulzii-Orshikh Dorj
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  2. Keun-Kwang Lee
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  3. Jae-Young Choi
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  4. Malrey Lee
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Corresponding authors

Correspondence to Keun-Kwang Lee, Jae-Young Choi or Malrey Lee.

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Dorj, UO., Lee, KK., Choi, JY. et al. The skin cancer classification using deep convolutional neural network. Multimed Tools Appl 77, 9909–9924 (2018). https://doi.org/10.1007/s11042-018-5714-1

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  • DOI: https://doi.org/10.1007/s11042-018-5714-1

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