Colorectal Cancer (CRC) is a prevalent and deadly disease, and accurate and timely diagnosis is essential for improving patient outcomes. The use of deep learning in medical imaging offers a promising avenue for achieving this goal. The ability to accurately identify different types of cancer cells can aid in treatment planning and prognosis and may ultimately help to save lives. This study proposes two models, radiomic-based Support Vector Machine (SVM) and a deep-learning model to recognize different types of cells in colorectal cancer using pathological images. In the first model, the radiomics features are extracted from the histopathology images and SVM used for CRC classification. The second model extracted the deep learning features and classified the CRC using Res-Net-18. The study utilized a dataset of 5000 pathological images of colorectal cancer, with eight classes of cells to be recognized. The deep learning model achieved high scores in terms of recall, precision, F1-score, and accuracy for each class, with an overall accuracy score of 0.95. These results demonstrate the potential of deep learning in medical imaging and cancer diagnosis. Our findings suggest that deep learning could be a powerful tool for accurately diagnosing different types of cancer cells, aiding in treatment planning and prognosis. Finally, this study contributes to the growing body of literature on the use of deep learning in medical imaging and cancer diagnosis. © 2023 IEEE.