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DenseMammoNet: An Approach for Breast Cancer Classification in Mammograms

  • Conference paper
  • First Online:
Fourth Congress on Intelligent Systems (CIS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 868))

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Abstract

In women all around the world, cancer of the breast is a condition that is both prevalent and on the rise. Breast cancer can be formed due to the lumps in the mammary region in females. Early detection of breast cancer masses (BCM) can save the lives of many women. In this paper, we have proposed an automated method that it is feasible to spot cancer of the breast in its infancy from mammographic images. To validate the authenticity of the proposed work, we have experimented with the publicly available dataset, Mammography Image Analysis Society (MIAS), and achieved an accuracy of 99.4%.

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Author information

Authors and Affiliations

  1. Centre for Advanced Studies, AKTU, Lucknow, Uttar Pradesh, 226021, India

    Shajal Afaq

  2. Dr. Vishwanath Karad, MIT-World Peace University, Kothrud, Pune, Maharashtra, 411038, India

    Anamika Jain

Authors
  1. Shajal Afaq
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  2. Anamika Jain
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Corresponding author

Correspondence to Shajal Afaq .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India

    Sandeep Kumar

  2. Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India

    Balachandran K.

  3. School of Civil, Environmental and Architectural Engineering, Anam-ro, Anam-dong, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  4. South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Afaq, S., Jain, A. (2024). DenseMammoNet: An Approach for Breast Cancer Classification in Mammograms. In: Kumar, S., K., B., Kim, J.H., Bansal, J.C. (eds) Fourth Congress on Intelligent Systems. CIS 2023. Lecture Notes in Networks and Systems, vol 868. Springer, Singapore. https://doi.org/10.1007/978-981-99-9037-5_12

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