Detection of presence or absence of metastasis in WSI patches of breast cancer using the dual-enhanced convolutional ensemble neural network

Abstract

Breast cancer (BC) is a prevalent malignancy worldwide, posing a significant public health burden due to its high incidence rate. Accurate detection is crucial for improving survival rates, and pathological diagnosis through biopsy is essential for detailed BC detection. Convolutional Neural Network (CNN)-based methods have been proposed to support this detection, utilizing patches from Whole Slide Imaging (WSI) combined with sophisticated CNNs. In this research, we introduced DECENN, a novel deep learning architecture designed to overcome the limitations of single CNN models under fixed pre-trained parameter transfer learning settings. DECENN employs an ensemble of VGG16 and DenseNet121, integrated with innovative modules such as Multi-Scale Feature Extraction, Heterogeneous Convolution Enhancement, Feature Harmonization and Fusion, and Feature Integration Output. Through progressive stages – from baseline models, intermediate DCNN and DCNN+ models, to the fully integrated DECENN model – significant performance improvements were observed in experiments using 5-fold cross-validation on the Patch Camelyon(PCam) dataset. DECENN achieved an AUC of 99.70% ± 0.12%, an F-score of 98.93% ± 0.06%, and an Accuracy of 98.92% ± 0.06%, (p<0.001). These results highlight DECENN’s potential to significantly enhance the automated detection and diagnostic accuracy of BC metastasis in biopsy specimens.

Keywords

• Deep learning
• Convolutional neural network
• Transfer learning
• Cancer detection