Predicting Pancreatic Cancer using AIML Techniques

Abstract

Abstract: The late diagnosis and low survival rates associated with pancreatic cancer make it a substantial global health challenge to detect at an early stage. The aim of this study is to explore the potential of deep learning methods in enhancing the early identification of pancreatic cancer. To build a convolutional neural network (CNN) model to detect early indications of pancreatic malignancies, we employed medical images from a computed tomography (CT) scan dataset. By illuminating the potential of deep learning as a tool, our discoveries bring hope for enhanced patient survival rates by aiding in the early diagnosis of pancreatic cancer. The importance of artificial intelligence in medical imaging and its revolutionary effects on cancer diagnosis are emphasized by this research. Pancreatic Tumor stands as a global leader in responsible death rates caused by cancer. Curing pancreatic cancer is possible when detected early