Abstract
This research introduces an improved method for identifying colorectal tumors through a combination of deep convolutional neural networks (CNNs), transfer learning, and sophisticated image processing techniques used on histopathological images. The suggested ensemble-based on ResNet50 and enhanced with a dual attention mechanism-surpasses individual model architectures by enhancing both accuracy and interpretability, allowing the model to emphasize crucial tissue areas pertinent to diagnosis. Segmentation techniques, such as watershed and distance transform, are utilized to define tumor margins and possible lesion regions. The dataset, obtained from Kather et al. (2019), includes 5,000 histopathological images spanning eight unique categories (tumor, stroma, complex, lymph, debris, mucosa, adipose, empty). The experimental findings demonstrate impressive results, achieving a training accuracy of 98.74%, a validation accuracy of 94.35%, an F1-score of 0.94, a recall of 0.94, a precision of 0.95, a specificity of 0.96, and a Cohen's kappa score of 0.9354, signifying outstanding inter-class consensus. These results showcase the model's strength across different class distributions and emphasize its possible clinical value in aiding the early identification and management of colorectal cancer.