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

作　　者：Sannasi Chakravarthy Bharanidharan Nagarajan Surbhi Bhatia Khan Vinoth Kumar Venkatesan Mahesh Thyluru Ramakrishna Ahlam AlMusharraf Khursheed Aurungzeb 

机构地区：[1]Department of Electronics and Communication Engineering,Bannari Amman Institute of Technology,Sathyamangalam,638402,India [2]School of Computer Science Engineering and Information Systems(SCORE),Vellore Institute of Technology,Vellore,632014,India [3]School of Science,Engineering and Environment,University of Salford,Manchester,M54WT,UK [4]Department of Computer Science&Engineering,Faculty of Engineering and Technology,JAIN(Deemed-to-be University),Bengaluru,562112,India [5]Department of Management,College of Business Administration,Princess Nourah Bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia [6]Department of Computer Engineering,College of Computer and Information Sciences,King Saud University,P.O.Box 51178,Riyadh,11543,Saudi Arabia [7]Adjunct Research Faculty,Centre for Research Impact&Outcome,Chitkara University,Rajpura,140401,India

出　　处：《Computers, Materials & Continua》2024年第9期5029-5045,共17页计算机、材料和连续体（英文）

基　　金：supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R432),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.

摘　　要：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.Maximumclassification accuracy of 97.585%(CBISDDSM),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.

关 键 词：EfficientNet MAMMOGRAMS breast cancer Explainable AI deep-learning transfer learning 

分 类 号：R73[医药卫生—肿瘤] TP18[医药卫生—临床医学]