Breast cancer is the most common cancer among women worldwide,posing significant diagnostic challenges.Traditional diagnostic techniques,while foundational,often lack precision and fail to provide clear insights into ...Breast cancer is the most common cancer among women worldwide,posing significant diagnostic challenges.Traditional diagnostic techniques,while foundational,often lack precision and fail to provide clear insights into their decision-making processes.This limitation underscores the need for advanced diagnostic tools that enhance both accuracy and interpretability.This study aims to integrate cutting-edge deep learning algorithms with Gradient-weighted Class Activation Mapping(Grad-CAM)to improve the accuracy and transparency of breast cancer diagnostics through mammographic analysis.We proposed robust approaches using MobileNet,Xception,and DenseNet models,enhanced with Grad-CAM,to analyze mammogram images.This integration facilitates a deeper understanding of model decisions,highlighting critical diagnostic features through visual explanations.The models were rigorously tested on the MIAS dataset to evaluate their diagnostic performance and reliability,achieving a diagnostic accuracy of 94.17%,demonstrating superior performance compared to traditional methods.The findings show significant potential for clinical application,promising to enhance patient outcomes through more accurate and transparent diagnostic practices in oncology.展开更多
基金supported by the National Key R&D Program of the China Project No.2020YFB2104402.
文摘Breast cancer is the most common cancer among women worldwide,posing significant diagnostic challenges.Traditional diagnostic techniques,while foundational,often lack precision and fail to provide clear insights into their decision-making processes.This limitation underscores the need for advanced diagnostic tools that enhance both accuracy and interpretability.This study aims to integrate cutting-edge deep learning algorithms with Gradient-weighted Class Activation Mapping(Grad-CAM)to improve the accuracy and transparency of breast cancer diagnostics through mammographic analysis.We proposed robust approaches using MobileNet,Xception,and DenseNet models,enhanced with Grad-CAM,to analyze mammogram images.This integration facilitates a deeper understanding of model decisions,highlighting critical diagnostic features through visual explanations.The models were rigorously tested on the MIAS dataset to evaluate their diagnostic performance and reliability,achieving a diagnostic accuracy of 94.17%,demonstrating superior performance compared to traditional methods.The findings show significant potential for clinical application,promising to enhance patient outcomes through more accurate and transparent diagnostic practices in oncology.
