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Texture Analysis Based Automated Decision Support System for Classification of Skin Cancer Using SA-SVM

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Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8835))

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Abstract

Early diagnosis of skin cancer is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system aimed to save lives, time and resources in the early diagnostic process. Segmentation, feature extraction, and lesion classification are the important steps in the proposed system. The system analyses the images to extract the affected area using a novel proposed segmentation method H-FCM-LS. A set of 45 texture based features is used. These underlying features which indicate the difference between melanoma and benign images are obtained through specialized texture analysis methods. For classification purpose, self-advising SVM is adapted which showed improved classification rate as compared to standard SVM. The diagnostic accuracy obtained through the proposed system is around 90% with sensitivity 91% and specificity 89%.

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Author information

Authors and Affiliations

  1. School of Electrical, Mechanical and Mechatronic Engineering, University of Technology, Sydney, Australia

    Ammara Masood, Adel Al-Jumaily & Khairul Anam

Authors
  1. Ammara Masood
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  2. Adel Al-Jumaily
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  3. Khairul Anam
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology Building, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chu Kiong Loo

  2. Department of Electronics and Communication Engineering, College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43009, Kajang, Selangor, Malaysia

    Keem Siah Yap

  3. School of Engineering and Information Technology, Murdoch University, South St., 6150, Murdoch, Western Australia, Australia

    Kok Wai Wong

  4. Department of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, 120-749, Seoul, South Korea

    Andrew Teoh

  5. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, SIP 215123, Suzhou, Jiangsu Province, China

    Kaizhu Huang

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© 2014 Springer International Publishing Switzerland

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Masood, A., Al-Jumaily, A., Anam, K. (2014). Texture Analysis Based Automated Decision Support System for Classification of Skin Cancer Using SA-SVM. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-12640-1_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12639-5

  • Online ISBN: 978-3-319-12640-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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