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Optimizing BI-RADS 4 Lesion Assessment Using Lightweight Convolutional Neural Network with CBAM in Contrast Enhanced Mammography

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Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care (Deep-Breath 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15451))

  • 14 Accesses

Abstract

Breast cancer is the leading cause of cancer-related mortality and morbidity among women worldwide. Early detection plays a crucial role in improving survival rates and BI-RADS classification is one of the effective ways of predicting breast cancer. However, BI-RADS Category 4 encompasses a broad spectrum of malignancy probabilities, ranging from over 2% to 95%. Due to the wide malignancy likelihood range and the ambiguous qualitative attributes of BI-RADS 4, patients are subjected to overdiagnosis and unnecessary procedures, such as biopsy, which entail a certain degree of physical trauma as well as financial strain. This study proposed a lightweight CNN where MobileNet serves as the backbone architecture, augmented with the Convolutional Block Attention Module (CBAM), resulting in the MobileNet-CBAM model. The model demonstrated good performance in discriminating BI-RADS 4 category malignant and benign cases in Contrast Enhanced Spectral Mammogram (CESM) with a prediction of 82%, 82% and 0.91 for accuracy, f1-score and roc-auc respectively. Additionally, for clinical friendliness, the model explanation was given using SHAP. Hence, the model presents potential utility in predicting breast cancer for lesions categorized as BI-RADS category 4 in breast imaging.

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Acknowledgments

This research is funded by the Modelling & Computation for Health And Society (MOCHAS) Postgraduate Research Training Programme (PRTP) Scholarship, Atlantic Technological University, Ireland .

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Authors and Affiliations

  1. Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Faculty of Engineering and Design, Atlantic Technological University, Sligo, Ireland

    Oladosu Oladimeji, Hamail Ayaz & Saritha Unnikrishnan

  2. Health and Biomedical Strategic Research Centre, Atlantic Technological University, Sligo, Ireland

    Oladosu Oladimeji, Hamail Ayaz & Saritha Unnikrishnan

  3. Center for Precision Engineering, Materials and Manufacturing Research (PEM), Faculty of Engineering and Design, Atlantic Technological university, F91 YW50, Sligo, Ireland

    Oladosu Oladimeji, Hamail Ayaz & Saritha Unnikrishnan

  4. Department of Computer Science and Applied Physics, Atlantic Technological university, Galway, Ireland

    Ian McLoughlin

Authors
  1. Oladosu Oladimeji
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  2. Hamail Ayaz
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  3. Ian McLoughlin
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  4. Saritha Unnikrishnan
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Corresponding author

Correspondence to Oladosu Oladimeji .

Editor information

Editors and Affiliations

  1. Radboudumc, Nijmegen, The Netherlands

    Ritse M. Mann

  2. Radboudumc, Nijmegen, The Netherlands

    Tianyu Zhang

  3. Macao Polytechnic University, Macao, China

    Tao Tan

  4. Radboudumc, Nijmegen, The Netherlands

    Luyi Han

  5. University Hospital Aachen, Aachen, Germany

    Danial Truhn

  6. Case Western Reserve University, Cleveland, OH, USA

    Shuo Li

  7. Netherlands Cancer Institute, Amsterdam, The Netherlands

    Yuan Gao

  8. Netherlands Cancer Institute, Amsterdam, The Netherlands

    Shannon Doyle

  9. University of Girona, Girona, Spain

    Robert Martí Marly

  10. Dresden University of Technology, Dresden, Germany

    Jakob Nikolas Kather

  11. Irving Medical Center, Columbia University, New York, NY, USA

    Katja Pinker-Domenig

  12. University of Pittsburgh, Pittsburgh, PA, USA

    Shandong Wu

  13. Radboudumc, Nijmegen, The Netherlands

    Geert Litjens

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Oladimeji, O., Ayaz, H., McLoughlin, I., Unnikrishnan, S. (2025). Optimizing BI-RADS 4 Lesion Assessment Using Lightweight Convolutional Neural Network with CBAM in Contrast Enhanced Mammography. In: Mann, R.M., et al. Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care. Deep-Breath 2024. Lecture Notes in Computer Science, vol 15451. Springer, Cham. https://doi.org/10.1007/978-3-031-77789-9_10

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  • DOI: https://doi.org/10.1007/978-3-031-77789-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-77788-2

  • Online ISBN: 978-3-031-77789-9

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