Colorectal cancer (CRC) is a leading worldwide cause of cancer-related mortality, and the role of prompt precise detection is of paramount interest in improving patient outcomes. Conventional diagnostic methods such as colonoscopy and histological examination routinely exhibit subjectivity, are extremely time-consuming, and are susceptible to variation. Through the development of digital pathology, deep learning algorithms have become a powerful approach in enhancing diagnostic precision and efficiency. In our work, we proposed a convolutional neural network architecture named MSRANetV2, specially optimized for the classification of colorectal tissue images. The model employs a ResNet50V2 backbone, extended with residual attention mechanisms and squeeze-and-excitation (SE) blocks, to extract deep semantic and fine-grained spatial features. With channel alignment and upsampling operations, MSRANetV2 effectively fuses multi-scale representations, thereby enhancing the robustness of the classification. We evaluated our model on a five-fold stratified cross-validation strategy on two publicly available datasets: CRC-VAL-HE-7K and NCT-CRC-HE-100K. The proposed model achieved remarkable average Precision, recall, F1-score, AUC, and test accuracy were 0.9884 plus-minus 0.0151, 0.9900 plus-minus 0.0151, 0.9900 plus-minus 0.0145, 0.9999 plus-minus 0.00006, and 0.9905 plus-minus 0.0025 on the 7K dataset. On the 100K dataset, they were 0.9904 plus-minus 0.0091, 0.9900 plus-minus 0.0071, 0.9900 plus-minus 0.0071, 0.9997 plus-minus 0.00016, and 0.9902 plus-minus 0.0006. Additionally, Grad-CAM visualizations were incorporated to enhance model interpretability by highlighting tissue areas that are medically relevant. These findings validate that MSRANetV2 is a reliable, interpretable, and high-performing architectural model for classifying CRC tissues.