A Comprehensive Review of Hybrid Morphological and Deep Learning Techniques for Pancreatic Cancer Detection and Stage Prediction

Abstract

Pancreatic cancer is one of the most aggressive and life-threatening malignancies, marked by late diagnosis, rapid progression, and poor survival rates. Accurate detection and stage prediction remain difficult due to the pancreas's complex anatomy, indistinct tumor boundaries, and variability in medical imaging data. Recent advancements in medical image analysis increasingly rely on integrating image processing techniques with deep learning models to improve diagnostic performance. This review evaluates existing morphological operations and deep learning architectures to detect, segment, and predict pancreatic cancer stages. Preprocessing techniques, such as erosion, dilation, opening, and closing, are widely used for noise removal, contrast enhancement, and boundary refinement. In addition to enhancing image quality, these methods facilitate the extraction of features more effectively. In medical images, deep learning models such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid CNN-Transformer frameworks have demonstrated strong capabilities to capture both local spatial features and global contextual relationships. A critical evaluation of state-of-the-art approaches is presented, along with their strengths and limitations. There are several key challenges identified, including the use of large annotated datasets, limited generalization across imaging modalities, and an insufficient integration of multi-modal data. Furthermore, most studies focus primarily on detection and segmentation, with relatively less attention paid to accurate stage-wise classification of pancreatic cancer. Several promising research directions are highlighted, including self-supervised learning, multimodal data fusion, explainable artificial intelligence, and 3D volumetric analysis. Overall, this review offers a structured overview of current advancements and identifies critical research gaps, which provides valuable insights for developing robust, efficient, and clinically applicable computer aided diagnostic systems for early detection and stage prediction of pancreatic cancer.