International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Pancreatic cancer is one of the most aggressive and life-threatening malignancies due to its late diagnosis, complex progression patterns, and limited treatment options. Traditional diagnostic approaches primarily rely on radiological interpretation and clinical biomarkers, which are often subjective and insufficient for early detection. To address these challenges, this paper proposes an Explainable Multimodal Artificial Intelligence (AI) system for the prediction and detection of pancreatic cancer.
The proposed system integrates CT/MRI medical imaging data with clinical and laboratory parameters to perform comprehensive cancer analysis. Advanced machine learning and deep learning techniques are employed to extract meaningful features from multimodal inputs, enabling accurate cancer stage prediction and survival estimation. Explainable AI (XAI) methods such as heatmaps and feature importance analysis are incorporated to enhance model transparency and clinical trust. The system is implemented using Python, Flask, and modern AI frameworks, providing a scalable web-based diagnostic platform. Experimental results demonstrate improved prediction accuracy, reduced diagnostic uncertainty, and enhanced interpretability, making the proposed system a reliable clinical decision-support tool.

Keywords: Pancreatic Cancer Prediction, Multimodal AI, Explainable AI, Medical Imaging, Clinical Data Analysis, Deep Learning

Downloads: | DOI: 10.17148/IJARCCE.2026.15196