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Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

Computers, Materials and Continua 2024年第3期80卷 5029-5045页

作者： Chakravarthy S. Nagarajan B. khan S.B. Venkatesan V.k. Ramakrishna M.T. Al Musharraf A. aurungzeb.k. Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam 638402 India School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology Vellore 632014 India School of Science Engineering and Environment University of Salford Manchester M54WT United Kingdom Adjunct Research Faculty Centre for Research Impact and Outcome Chitkara University Rajpura 140401 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bengaluru 562112 India Department of Management College of Business Administration Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Breast cancer is a type of cancer responsible for higher mortality rates among women. The cruelty of breast cancer always requires a promising approach for its earlier detection. In light of this, the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast tumors. In addition, the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input mammogram. Accordingly, the work proposed an EfficientNetB0 having a Spatial Attention Layer with XGBoost (ESA-XGBNet) for binary classification of mammograms. For this, the work is trained, tested, and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM, INbreast, and MIAS databases. Maximum classification accuracy of 97.585% (CBIS-DDSM), 98.255% (INbreast), and 98.91% (MIAS) is obtained using the proposed ESA-XGBNet architecture as compared with the existing models. Furthermore, the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique. © 2024 The Authors.

关键词： breast cancer deep-learning EfficientNet Explainable AI mammograms transfer learning

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