Background: Breast cancer poses a significant global health challenge, characterized by complex origins and the potential for life-threatening metastasis. The critical need for early and accurate detection is underscored by the 685,000 lives claimed by the disease worldwide in 2020. Deep learning has made strides in advancing the prompt diagnosis of breast cancer. However, obstacles persist, such as dealing with high-dimensional data and the risk of overfitting, necessitating fresh approaches to improve accuracy and real-world applicability.
Methods: In response to these challenges, we propose BCED-Net, which stands for Breast Cancer Ensemble Diagnosis Network. This innovative framework leverages transfer learning and the extreme gradient boosting (XGBoost) classifier on the Breast Cancer RSNA dataset. Our methodology involved feature extraction using pre-trained models-namely, Resnet50, EfficientnetB3, VGG19, Densenet121, and ConvNeXtTiny-followed by the concatenation of the extracted features. Our most promising configuration combined features extracted from deep convolutional neural networks-namely Resnet50, EfficientnetB3, and ConvNeXtTiny-that were classified using the XGBoost classifier.
Results: The ensemble approach demonstrated strong overall performance with an accuracy of 0.89. The precision, recall, and F1-score values, which were all at 0.86, highlight a balanced trade-off between correctly identified positive instances and the ability to capture all actual positive samples.
Conclusion: BCED-Net represents a significant leap forward in addressing persistent issues such as the high dimensionality of features and the risk of overfitting.
Keywords: Breast cancer classification; Feature extraction and concatenation; Performance evaluation; XGBoost classifier.