An Explainable Hybrid Deep Learning Approach for Histopathological Cancer Diagnosis

Proper histopathological cancer classification is vital for early diagnosis; however, this is not easy due to the variations in tissue types. In this research, the hypothesis is that a hybrid deep learning model can be developed by combining DenseNet121 and Swin Transformer Tiny using a Cross-Attention Fusion Mechanism, which will combine local texture features with extensive global contextual representations. The model was trained and tested on the Lung and Colon Cancer Histopathological Image Dataset, which consisted of five classes of tissues. Generalization was improved by applying standard preprocessing techniques, including resizing, normalization, and MixUp augmentation. According to experimental findings, the hybrid model achieves a model accuracy of 99.84%, a Cohen's Kappa of 0.993, which is better than that of conventional CNNs (ResNet18, DenseNet121, ConvNeXt-Tiny) and transformer models (Swin-Small). An additional indication that the model emphasizes diagnostically important areas is the use of score-CAM visualizations. These results suggest that the proposed hybrid cross-attention architecture provides a precise and comprehensible framework for cancer diagnosis using AI on histopathological images.