Detection and Classification of Pancreatic Cancer Nodules on CT Images using U-Net and Ensemble Models

INTRODUCTION: Pancreatic Adenocarcinoma (PDAC) is the fourth leading cause of cancer-related mortality, predominantly affecting individuals over 45 years. Conventional diagnostic imaging modalities such as CT, MRI, PET, and ultrasound remain limited in detecting early-stage disease. Advances in deep learning (DL) hold promise for enhanced segmentation and early detection of pancreatic tumors. METHODS: This study proposes an AI-based framework for the early detection of PDAC from CT images. Noise reduction was performed using a Boosted Adaptive Diffusion Filter (BADF). Tumor segmentation was achieved with a modified U-Net model, optimized using the Adam optimizer. Classification was conducted through an ensemble learning approach. The framework was validated on publicly available CT datasets, with performance assessed using standard evaluation metrics. RESULTS: The proposed model achieved 98.7% accuracy, 98.7% precision, 97.92% specificity, and 99.63% AUC in distinguishing pancreatic cancers, including tumors smaller than 2 cm. These results demonstrate superior performance compared to existing DL-based approaches. DISCUSSION: Integration of BADF preprocessing, U-Net-based segmentation, and ensemble classification enhanced model robustness and detection accuracy. The framework addresses challenges in small tumor detection, a critical factor in improving clinical outcomes. CONCLUSION: The proposed method demonstrates significant potential for early PDAC detection using CT images. By combining advanced preprocessing, segmentation, and ensemble learning, the framework enhances diagnostic accuracy and reliability, supporting clinical decision-making and contributing to improved patient care.