ToBaFu: Topology-based fusion model for classification of two-dimensional cancer images

Medical images play a pivotal role in disease diagnosis. Numerous studies on cancer image analysis focus on end-to-end deep neural networks, neglecting the analysis of global topological features in images. In cancer diagnosis, pathological images frequently display structures like holes or loops that are absent in healthy images, highlighting the benefits of topological analysis of images. In our study, we employ persistent homology (PH) to extract topological features from two-dimensional cancer images. Then, we propose a topology-based model (Topo) for image classification by implementing a shallow neural module following the feature extraction. More importantly, we integrate the Topo model with an end-to-end enhanced ResNet architecture to develop a novel topology-based fusion model (ToBaFu), aimed at enhancing diagnostic performance and model robustness. The proposed ToBaFu model achieves remarkable performance across three cancer image datasets: 99.98 % accuracy and F1-score on the LC-25000 lung and colon cancer histopathological dataset, 99.60 % accuracy and F1-score on the CRC-5000 colorectal cancer histological dataset, and 99.80 % accuracy with 99.83 % F1-score on the BUS-250 breast ultrasound dataset.