Breast cancer,particularly Invasive Ductal Carcinoma(IDC),is a primary global health concern predominantly affecting women.Early and precise diagnosis is crucial for effective treatment planning.Several AI-based tech-...Breast cancer,particularly Invasive Ductal Carcinoma(IDC),is a primary global health concern predominantly affecting women.Early and precise diagnosis is crucial for effective treatment planning.Several AI-based tech-niques for IDC-level classification have been proposed in recent years.Processing speed,memory size,and accuracy can still be improved for better performance.Our study presents ECAM,an Enhanced Channel-Wise Attention Mechanism,using deep learning to analyze histopathological images of Breast Invasive Ductal Carcinoma(BIDC).The main objectives of our study are to enhance computational efficiency using a Separable CNN architecture,improve data representation through hierarchical feature aggregation,and increase accuracy and interpretability with channel-wise attention mechanisms.Utilizing publicly available datasets,DataBioX IDC and the BreakHis,we benchmarked the proposed ECAM model against existing state-of-the-art models:DenseNet121,VGG16,and AlexNet.In the IDC dataset,the model based on AlexNet achieved an accuracy rate of 86.81%and an F1 score of 86.94%.On the other hand,DenseNet121 outperformed with an accuracy of 95.60%and an F1 score of 95.75%.Meanwhile,the VGG16 model achieved an accuracy rate of 91.20%and an F1 score of 90%.Our proposed ECAM model outperformed the state-of-the-art,achieving an impressive F1 score of 96.65%and an accuracy rate of 96.70%.The BreakHis dataset,the AlexNet-based model,achieved an accuracy rate of 90.82%and an F1 score of 90.77%.DenseNet121 achieved a higher accuracy rate of 92.66%with an F1 score of 92.72%,while the VGG16 model achieved an accuracy of 92.60%and an F1 score of 91.31%.The proposed ECAM model again outperformed,achieving an F1 score of 96.37%and an accuracy rate of 96.33%.Our model is a significant advancement in breast cancer diagnosis,with high accuracy and potential as an automated grading,especially for IDC.展开更多
文摘Breast cancer,particularly Invasive Ductal Carcinoma(IDC),is a primary global health concern predominantly affecting women.Early and precise diagnosis is crucial for effective treatment planning.Several AI-based tech-niques for IDC-level classification have been proposed in recent years.Processing speed,memory size,and accuracy can still be improved for better performance.Our study presents ECAM,an Enhanced Channel-Wise Attention Mechanism,using deep learning to analyze histopathological images of Breast Invasive Ductal Carcinoma(BIDC).The main objectives of our study are to enhance computational efficiency using a Separable CNN architecture,improve data representation through hierarchical feature aggregation,and increase accuracy and interpretability with channel-wise attention mechanisms.Utilizing publicly available datasets,DataBioX IDC and the BreakHis,we benchmarked the proposed ECAM model against existing state-of-the-art models:DenseNet121,VGG16,and AlexNet.In the IDC dataset,the model based on AlexNet achieved an accuracy rate of 86.81%and an F1 score of 86.94%.On the other hand,DenseNet121 outperformed with an accuracy of 95.60%and an F1 score of 95.75%.Meanwhile,the VGG16 model achieved an accuracy rate of 91.20%and an F1 score of 90%.Our proposed ECAM model outperformed the state-of-the-art,achieving an impressive F1 score of 96.65%and an accuracy rate of 96.70%.The BreakHis dataset,the AlexNet-based model,achieved an accuracy rate of 90.82%and an F1 score of 90.77%.DenseNet121 achieved a higher accuracy rate of 92.66%with an F1 score of 92.72%,while the VGG16 model achieved an accuracy of 92.60%and an F1 score of 91.31%.The proposed ECAM model again outperformed,achieving an F1 score of 96.37%and an accuracy rate of 96.33%.Our model is a significant advancement in breast cancer diagnosis,with high accuracy and potential as an automated grading,especially for IDC.
