Hybrid Loss-Based Deep Learning Framework Using EfficientNet-B3 for Multi-Class Colorectal Cancer Detection

Diagnosis of colorectal cancer (CRC) primarily relies on histopathological examination of hematoxylin and eosin-stained tissue sections; however, manual interpretation is time-consuming, subjective, and increasingly impractical given the rapid growth of digital pathology data. We introduced a hybrid loss-based learning framework for multi-class colorectal histopathology image classification that improves class-balanced performance without increasing model complexity. Various EfficientNet versions were checked as the first step to establishing a strong baseline, and EfficientNet-B3 was chosen based on validation Matthews Correlation Coefficient (MCC). Extending this backbone, we propose a hybrid loss function that mixes weighted cross-entropy and focal loss to achieve the combined effect of dealing with the global class imbalance while also focusing on hard-to-classify samples. The results of experiments on a large-scale colorectal histopathology dataset show that the Hybrid-B3 model introduced significantly improves the baseline settings. Hybrid-B3 registers a test accuracy of 99.83%, a very high class-balanced performance with a balanced accuracy and G-Mean of 99.85%. The changes are verified and non-random by the statistical validation using bootstrap confidence intervals and paired significance tests. The offered solution emphasizes the efficiency of loss-function optimization solely to provide improvements in robustness and reliability in computational pathology and, correspondingly, yields a practical and scalable solution for colorectal cancer diagnostic support in the real world.