DEEP LEARNING BASED COLORECTAL CANCER CLASSIFICATION
Keywords:
Colorectal cancer, deep learning, CNN, MobileNet, DenseNet, EfficientNet, LC25000.Abstract
Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide. Conventional histopathological diagnosis, while effective, is labor-intensive, subjective, and prone to inter-observer variability. Recent advances in artificial intelligence, particularly deep learning, offer the potential to augment diagnostic workflows with automated, accurate, and interpretable image classification systems. This study develops and evaluates a deep learning-based framework for binary classification of CRC using histopathological images from the LC25000 dataset. Three convolutional neural network architectures—MobileNet, DenseNet, and EfficientNet—were implemented with transfer learning, data augmentation, and Grad-CAM interpretability tools. Among these, MobileNet demonstrated a balance of high accuracy (99.37%) and computational efficiency, highlighting its suitability for real-time clinical deployment. This work underscores the promise of lightweight deep learning models in supporting early CRC detection and provides a foundation for scalable AI-assisted pathology solutions.
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