ABD-TDA:Attention-Based Deep and Topological Feature Fusion for Histopathological Cancer Classification

Accurate histopathological image classification remains challenging due to substantial morphological and structural variability across cancer types. While Vision Transformers (ViTs) capture global visual information through self-attention, they do not explicitly represent the topological organization of tissue structures. We propose ABD-TDA, a deep and topological feature fusion framework that combines transformer-based visual features with complementary topological descriptors. A pretrained ViT extracts a 768-dimensional visual representation, while a dedicated topological branch processes 1,200 Betti-0 and Betti-1 features derived from RGB and HSV color spaces. The resulting 64-dimensional topological representation is fused with the ViT features for classification. We evaluate ABD-TDA on the ICIAR2018 breast cancer and UT-Osteosarcoma bone cancer datasets, containing four and three classes, respectively. The framework achieves 94.10% and 96.54% accuracy, with macro-AUC scores of 99.42% and 99.65%, respectively. Ablation results show consistent improvements from feature fusion, increasing accuracy from 92.67% with ViT alone to 94.10% on ICIAR2018 and from 94.15% to 96.54% on UT-Osteosarcoma. These results demonstrate that topological descriptors provide complementary structural information to transformer-based visual features, leading to more discriminative representations for histopathological cancer classification.