Deep Learning Models for Early Diagnosis of Pancreatic Cancer

Poor patient outcomes result from pancreatic cancer, a very fatal illness that is often detected at an advanced stage. The pursuit of new diagnostic tools is vital, since it is crucial to improve survival rates via early diagnosis. In this work, we investigate the use of deep learning models to pancreatic cancer early detection. First, we review the basic principles of pancreatic cancer and the difficulties in detecting it at an early stage. Next, we provide a thorough introduction to deep learning, highlighting its applications in early illness detection and medical image analysis. Moving further, In our discussion on the value of robust datasets for deep learning model training and validation, we emphasize the need of having large, meticulously annotated pancreatic cancer-specific datasets. We explore a variety of deep learning designs that are optimised for early diagnosis, including recurrent neural networks (RNNs) and convolutional neural networks (CNNs). We analyze their performance, comparing and contrasting their strengths and weaknesses in detecting pancreatic cancer at different stages. We also discuss interpretability, illuminating deep learning models' opaque nature and offering solutions to increase transparency.