Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer-related death worldwide, with about 1.85 million cases and 850,000 deaths annually. While a definitive diagnosis depends on the histopathological examination of hematoxylin and eosin-stained tissue slides, considerable variability remains in slide interpretation among pathologists, and manual examination is time-consuming. These limitations have led to the development of computer-aided methods for analyzing colorectal histopathology slides. Recent hybrid Transformer models often lack adaptive channel re-weighting, use fixed window sizes that may restrict the multi-scale receptive field, or rely on traditional feature-fusion techniques that assume simple element-wise addition. To overcome these shortcomings, DualPath-CRC proposes three architectural innovations: (i) Attention-Augmented InceptionNeXt blocks, which apply sigmoid-based channel attention to multi-branch depthwise convolutions; (ii) Adaptive Window Swin Transformer blocks, in which a Window-Size Prediction Network adaptively controls the receptive field of self-attention; and (iii) a Gated Cross-Pathway Fusion module, which fuses the local and global feature streams through learned sigmoid gating. For the classification of histological tissue patches, DualPath-CRC achieves an accuracy of 99.98% on NCT-CRC-HE-100K and 99.34% on Kather-5K. The regions highlighted by Grad-CAM, Grad-CAM++, and LIME analyses provide qualitative information about which parts of the image are important for the model’s predictions and typically correspond to morphologically relevant tissue areas.