The use of Artificial Intelligence (AI) for analyzing histopathological images has emerged as a dynamic field in medical diagnostics, particularly in pathology. Traditionally, the analysis of histopathological images requires significant effort from pathologists to accurately assess cellular characteristics. However, the advent of AI and machine learning has led to notable improvements in diagnostic reliability through automated and simplified analysis processes. In this study, we implemented and compared two convolutional neural network models trained on a multiclass dataset of histopathological images associated with colorectal cancer. Following evaluation, the VGG-19 model demonstrated superior performance, achieving a precision rate of 98%. The researchers employed the “Grad-CAM" technique, a Python-based graphical user interface, to understand the model’s classification process and highlight the salient areas during training. Furthermore, the application of AI to colon histopathological images holds promise for enhancing early cancer diagnosis and improving patient outcomes. By integrating AI with intuitive user interfaces like Miky, pathologists can streamline their analysis workflows and augment diagnostic accuracy.