Development and estimation of the models for early prediction of pancreatic cancer using deep learning

The development of efficient diagnostic tools for the early detection of pancreatic cancer is critical due to the disease's high mortality rate and often late diagnosis. This research focuses on leveraging deep learning models, particularly convolutional neural networks (CNN) and YOLO-based architectures, to enhance the accuracy of cancer detection using medical imaging. The article reviews existing techniques and proposes a novel approach to image classification for identifying cancerous growths in the pancreas. The results demonstrate improved accuracy in diagnosis, highlighting the potential for early intervention.