Colorectal Cancer (CRC) is a malignant tumor located in the colon or rectum region, and it is the second leading cause of death globally between cancer types. Early detection often allows for more treatment options. With the increasing rates of colon samples, the diagnosis workload has increased on pathologists, which might risk a wrong clinical diagnosis. Wrong clinical diagnosis could lead to an accidental loss of life, and this is where the role of Deep Learning (DL) to aid in early medical detection is of great significance to increase the probabil- ity of survival. DL is the primary location of never-ending medical data where it employs this data into useful knowledge. Its performance in medical diagnosis is exceptional. In our project, we aim to improve on the histopathological colon cancer detection model by employing DL and Convolutional Neural Networks (CNN) with a higher level of performance and refinement to improve on previous studies. We also examine the effect of Stain Normalization (SN) on histopathology images and how it improved the performance of our model.