Colorectal cancer (CRC), accounting for 10% of global cancer cases and being the third most prevalent type, is expected to see a significant increase in the coming years. This surge underscores the need for precise diagnostics. Effective treatment relies on accurate histopathological analysis of hematoxylin and eosin (H&E) stained biopsies, which is critical for recommending minimally invasive treatments. However, manual evaluations of these biopsies are labor-intensive and error-prone due to staining variations and inconsistencies, complicating the tasks of pathologists. To address these challenges, advanced automated image analysis, including deep learning with convolutional neural networks (CNNs) and machine learning (ML) techniques, has significantly enhanced computer-aided diagnosis systems. Consequently, this paper proposes a composite model that combines deep learning and machine learning to improve colorectal cancer diagnosis accuracy. Specifically, the model aims to increase diagnostic precision, reduce complexity and computing demands, and effectively prevent overfitting for reliable performance. Therefore, the proposed cascaded design includes feature extraction using MobileNetV2 and DenseNet121 via transfer learning (TL), data distribution balancing in the Extended Bioimaging Histopathological Image Segmentation (EBHI-Seg) dataset using the Synthetic Minority Over-sampling Technique (SMOTE), key feature selection using a Chisquare test, classification by machine learning algorithms, and improving classification accuracy through hyperparameter tuning. Finally, the results evaluated on the available EBHI-Seg dataset achieve 97.28% accuracy, 97.29% precision, 97.27% recall, 96.27% F1- score, and 99.4% area under the curve (AUC), demonstrating that the suggested model is superior to other methods already in use.
