This study introduces an innovative deep learning framework to address the limitations of traditional pathological image analysis and the pressing demand for medical resources in tumor diagnosis. With the global rise in cancer cases, manual examination by pathologists is increasingly inadequate, being both time-consuming and subject to the scarcity of professionals and individual subjectivity, thus impacting diagnostic accuracy and efficiency. Deep learning, particularly in computer vision, offers significant potential to mitigate these challenges. Automated models can rapidly and accurately process large datasets, revolutionizing tumor detection and classification. However, existing methods often rely on single attention mechanisms, failing to fully exploit the complexity of pathological images, especially in extracting critical features from whole-slide images. We developed a framework incorporating a cascaded attention mechanism, enhancing meaningful pattern recognition while suppressing irrelevant background information. Experiments on the Camelyon16 dataset demonstrate superior classification accuracy, model generalization, and result interpretability compared to state-of-the-art techniques. This advancement promises to enhance diagnostic efficiency, reduce healthcare costs, and improve patient outcomes.