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Colour Clustering and Deep Transfer Learning Techniques for Breast Cancer Detection Using Mammography Images

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The Latest Developments and Challenges in Biomedical Engineering (PCBEE 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 746))

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Abstract

Breast cancer is a major global health concern affecting millions of women each year. Computer-aided diagnosis (CAD) systems have the potential to contribute significantly to early detection and reducing the mortality rate of breast cancer. This paper proposes a new methodology for breast cancer detection utilising data analytics, artificial intelligence, and mammograms. The approach is a mixed methodology based on colour clustering and deep transfer learning techniques to extract features from mammogram images. The proposed method was validated using the mini-DDSM mammogram images dataset, and its effectiveness was evaluated using various metrics such as accuracy, specificity, precision, recall, and F1 score. The results showed that all networks had high detection accuracy, with GoogleNet achieving the highest (99.58%) and ShuffleNet the lowest (97.08%). The proposed method achieved 100% detection accuracy using ResNet18, VGG16, ShuffleNet, DarkNet, and NasnetLarge, while Inception-ResNet-v2 had a detection accuracy of 98.33% with LRC and 99.17% with SVM. The proposed method has demonstrated the potential to improve the performance of CAD systems.

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Authors and Affiliations

  1. Department of Electronic and Electrical Engineering, Brunel University London, London, UB8 3PH, UK

    Hosameldin O. A. Ahmed & Asoke K. Nandi

  2. OpenAITech LTD, Old Marylebone, London, NW1 5RA, UK

    Hosameldin O. A. Ahmed

Authors
  1. Hosameldin O. A. Ahmed
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  2. Asoke K. Nandi
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Correspondence to Asoke K. Nandi .

Editor information

Editors and Affiliations

  1. Institute of Electronics, Lodz University of Technology, Lodz, Poland

    Paweł Strumiłło

  2. Institute of Electronics, Lodz University of Technology, Lodz, Poland

    Artur Klepaczko

  3. Institute of Electronics, Lodz University of Technology, Lodz, Poland

    Michał Strzelecki

  4. Institute of Materials Science, Lodz University of Technology, Lodz, Poland

    Dorota BociÄ…ga

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Ahmed, H.O.A., Nandi, A.K. (2024). Colour Clustering and Deep Transfer Learning Techniques for Breast Cancer Detection Using Mammography Images. In: Strumiłło, P., Klepaczko, A., Strzelecki, M., Bociąga, D. (eds) The Latest Developments and Challenges in Biomedical Engineering. PCBEE 2023. Lecture Notes in Networks and Systems, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-031-38430-1_9

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  • DOI: https://doi.org/10.1007/978-3-031-38430-1_9

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