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
This paper proposes an accurate,efficient and explainable method for the classification of the surrounding rock based on a convolutional neural network(CNN).The state-of-the-art robust CNN model(EfficientNet)is applie...This paper proposes an accurate,efficient and explainable method for the classification of the surrounding rock based on a convolutional neural network(CNN).The state-of-the-art robust CNN model(EfficientNet)is applied to tunnel wall image recognition.Gaussian filtering,data augmentation and other data pre-processing techniques are used to improve the data quality and quantity.Combined with transfer learning,the generality,accuracy and efficiency of the deep learning(DL)model are further improved,and finally we achieve 89.96%accuracy.Compared with other state-of-the-art CNN architectures,such as ResNet and Inception-ResNet-V2(IRV2),the presented deep transfer learning model is more stable,accurate and efficient.To reveal the rock classification mechanism of the proposed model,Gradient-weight Class Activation Map(Grad-CAM)visualizations are integrated into the model to enable its explainability and accountability.The developed deep transfer learning model has been applied to support the tunneling of the Xingyi City Bypass in the high mountain area of Guizhou,China,with great results.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.
文摘This paper proposes an accurate,efficient and explainable method for the classification of the surrounding rock based on a convolutional neural network(CNN).The state-of-the-art robust CNN model(EfficientNet)is applied to tunnel wall image recognition.Gaussian filtering,data augmentation and other data pre-processing techniques are used to improve the data quality and quantity.Combined with transfer learning,the generality,accuracy and efficiency of the deep learning(DL)model are further improved,and finally we achieve 89.96%accuracy.Compared with other state-of-the-art CNN architectures,such as ResNet and Inception-ResNet-V2(IRV2),the presented deep transfer learning model is more stable,accurate and efficient.To reveal the rock classification mechanism of the proposed model,Gradient-weight Class Activation Map(Grad-CAM)visualizations are integrated into the model to enable its explainability and accountability.The developed deep transfer learning model has been applied to support the tunneling of the Xingyi City Bypass in the high mountain area of Guizhou,China,with great results.
