@Article{cmc.2024.052531,
AUTHOR = {Sannasi Chakravarthy, Bharanidharan Nagarajan, Surbhi Bhatia Khan, Vinoth Kumar Venkatesan, Mahesh Thyluru Ramakrishna, Ahlam Al Musharraf, Khursheed Aurungzeb},
TITLE = {Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {80},
YEAR = {2024},
NUMBER = {3},
PAGES = {5029--5045},
URL = {http://www.techscience.com/cmc/v80n3/57861},
ISSN = {1546-2226},
ABSTRACT = {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 EfficientNet-B0 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.},
DOI = {10.32604/cmc.2024.052531}
}