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Automatic Sub Classification of Benign Breast Tumor

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Smart Trends in Systems, Security and Sustainability

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 18))

Abstract

This paper describes about automatic classification of benign lesions. Our re-search work mostly aims at development of a data driven effective classification system to determine type of benign breast tumors. The existing system in medical field identifies the type of benign breast tumor based on histopathological report obtained after a painful surgical process. Our target is to develop a system that would work without histopathological attributes to avoid pains to the patient. Our focus was to eliminate the role of histopathological attributes in the detection of benign tumor type. So we tried to identify correlation of histopathological features with mammographic image features and patient history features in order to explore if histopathological features can be replaced by corresponding correlated features. With replaced attributes we gain training accuracy for J48 as 79.78% and with SVM 81.91%. We obtained testing accuracy for J48 and SVM as 100% and 90.90% respectively.

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Acknowledegment (Ethical Approval)

The authors desires to express gratitude towards Dr. U. V. Takalkar [M.S. (Gen.Surg.) M.E.D.S FUICC (SWITZERLAND) FAIS, MSSAT (USA), Cancer, Chairman, Kodlikeri Memorial Hospital] for their backing and priceless guidance. Images were offered by Kodlikeri memorial Hospital, Aurangabad and Hedgewar Hospital, Aurangabad, Maharashtra, India.

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Authors and Affiliations

  1. School of Computer Sciences, North Maharashtra University, Jalgaon, India

    Aparna Bhale & Manish Joshi

Authors
  1. Aparna Bhale
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  2. Manish Joshi
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Correspondence to Aparna Bhale .

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Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, United Kingdom

    Xin-She Yang

  2. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya K. Nagar

  3. Sabar Institute of Technology, Gujarat Technological University, Gujarat, India

    Amit Joshi

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Bhale, A., Joshi, M. (2018). Automatic Sub Classification of Benign Breast Tumor. In: Yang, XS., Nagar, A., Joshi, A. (eds) Smart Trends in Systems, Security and Sustainability. Lecture Notes in Networks and Systems, vol 18. Springer, Singapore. https://doi.org/10.1007/978-981-10-6916-1_20

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  • DOI: https://doi.org/10.1007/978-981-10-6916-1_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6915-4

  • Online ISBN: 978-981-10-6916-1

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