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Transfer Learning of Mammogram Images Using Morphological Bilateral Subtraction and Enhancement Filter

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Key Digital Trends Shaping the Future of Information and Management Science (ISMS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 671))

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Abstract

Deep Learning in healthcare immediately affects a person’s health and well-being. Deep Learning in medicine helps in early diagnosis. Medical data retrieval is time-consuming. According to a study, breast cancer is more common among women than men. Mammography is a frequent computer-assisted diagnosis of breast cancer. Mammography isn’t always accurate despite being widely used. Only 30% of breast cancers are detected early. In this post, we’ll look at the second step of preprocessing, which involves adding a filter to the image once it’s been split up. To enhance the appearance, breast-border-erasing filtering techniques are used for the data. The proposed segmentation approach subtracts two pictures after utilizing morphological procedures to restore the breast image’s boundaries. We found that employing homomorphic filters and a modified bi-level histogram to identify noise and boost contrast improved picture quality.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences (A), Visakhapatnam, 430049, Andhra Pradesh, India

    N. Marline Joys Kumari

  2. Department of Computer Science and Engineering, Vignan’s Institute of Information Technology (A), Visakhapatnam, 430049, Andhra Pradesh, India

    N. Thirupathi Rao

  3. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, 522302, Andhra Pradesh, India

    Debnath Bhattacharyya

  4. Department of Computer Information System (CIS), Faculty of Information Communication Technology (ICT), University of Malta, Msida, 2080, Malta

    Lalit Garg

  5. School of Computing, DIT University, Dehradun, India

    Megha Bhushan

Authors
  1. N. Marline Joys Kumari
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  2. N. Thirupathi Rao
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  3. Debnath Bhattacharyya
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  4. Lalit Garg
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  5. Megha Bhushan
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Corresponding author

Correspondence to N. Marline Joys Kumari .

Editor information

Editors and Affiliations

  1. Faculty of Information and Communication Technology, University of Malta, Msida, Malta

    Lalit Garg

  2. Department of Computer Science and Engineering, National Institute of Technology Raipur, Chhatisgarh, India

    Dilip Singh Sisodia

  3. Central University of Rajasthan, Tehsil Kishangarh, Rajasthan, India

    Nishtha Kesswani

  4. CIS Department, University of Malta, Msida, Malta

    Joseph G. Vella

  5. EMLYON Business School, Écully, France

    Imene Brigui

  6. Department of Applied Data Science, Institute of Energy Technology, Halden, Norway

    Sanjay Misra

  7. Computer Science and Engineering, National Institute of Technology, Chhattisgarh, India

    Deepak Singh

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Kumari, N.M.J., Rao, N.T., Bhattacharyya, D., Garg, L., Bhushan, M. (2023). Transfer Learning of Mammogram Images Using Morphological Bilateral Subtraction and Enhancement Filter. In: Garg, L., et al. Key Digital Trends Shaping the Future of Information and Management Science. ISMS 2022. Lecture Notes in Networks and Systems, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-031-31153-6_4

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