Medical Image Processing for Pancreatic Cancer Detection Using Deep Learning

Pancreatic cancer remains one of the most difficult and lethal types of cancer due to its mild early symptoms and late-stage detection, which frequently limits treatment options and lowers survival rates. The development of deep learning and artificial intelligence in recent years has transformed tumor detection and medical imaging, opening up new avenues for quicker and more precise diagnosis.This study introduces a clever, intuitive, computer-aided diagnosis approach that uses convolutional neural networks (CNNs) to detect pancreatic cancer early. To identify if an image is malignant or not, the model is trained on CT and MRI scans, utilizing the capability of the VGG16 deep learning architecture.The Grad-CAM (Gradient-weighted Class Activation Mapping) feature of the system, which uses aesthetically pleasing heatmaps to emphasize the tumor-affected areas of the scan, significantly improves interpretability and confidence in AI-driven conclusions. The approach is implemented using a simple Flask web application that enables users or medical professionals to upload scan photos, view the heatmap overlays, receive categorization findings, and get health precautions. With the help of state-of-the-art deep learning methods and a real-time interactive platform, this initiative seeks to assist radiologists and other healthcare professionals in reaching quicker and better diagnoses. Ultimately, this approach offers a promising first step toward incorporating artificial intelligence into standard medical processes by enhancing clinical outcomes and promoting early cancer diagnosis.