TY  - EJOU
AU  - Chakravarthy, Sannasi 
AU  - Nagarajan, Bharanidharan 
AU  - Khan, Surbhi Bhatia 
AU  - Venkatesan, Vinoth Kumar 
AU  - Ramakrishna, Mahesh Thyluru 
AU  - Musharraf, Ahlam Al 
AU  - Aurungzeb, Khursheed 

TI  - Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 80
IS  - 3
SN  - 1546-2226

AB  - 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.
KW  - EfficientNet; mammograms; breast cancer; Explainable AI; deep-learning; transfer learning

DO  - 10.32604/cmc.2024.052531