AI Enhanced Medical Image Analysis for Early Pancreatic Cancer Detection

Pancreatic cancer is among the most lethal malignancies worldwide, primarily due to its frequent diagnosis at advanced stages, which contributes to a notably low five-year survival rate. The organ’s deep anatomical position within the abdominal cavity, often concealed by surrounding structures, poses significant challenges for early clinical detection through conventional examinations. However, early identification is achievable with advanced medical imaging modalities like computed tomography (CT) and magnetic resonance imaging (MRI). Recent developments in Computer-Aided Diagnosis (CAD) systems have demonstrated encouraging potential in enhancing the detection of pancreatic cancer at an earlier stage. This study introduces a comprehensive framework that extends current CAD methodologies by incorporating five integrated components: image preprocessing, segmentation, feature extraction, classification, and explainable artificial intelligence (XAI). The preprocessing stage enhances image clarity using color transformation and isotropic diffusion filtering. For segmentation, a U-Net-based neural architecture effectively delineates tumor regions. Subsequent feature extraction, performed using a ResNet-50 model, captures key image attributes such as contrast, correlation, and dissimilarity. A hybrid classification model, combining Deep Convolutional Neural Networks (DCNN) and Deep Belief Networks (DBN), is employed to distinguish between malignant and benign tissues. To promote transparency and clinical acceptance, interpretability tools like Grad-CAM and SHAP are integrated, offering visual and statistical insights into model decision-making