Evaluating deep learning models for pancreatic cancer diagnosis

Pancreatic cancer is a highly aggressive and often fatal disease, with early detection being a key factor for improving patient survival. Recent advances in artificial intelligence (AI), particularly deep learning, have demonstrated significant potential in disease diagnosis based on histopathological images. This study investigates the effectiveness of two deep learning models, residual neural network (ResNet) and visual geometry group network (VGG), in distinguishing pancreatic cancer tissue from normal pancreatic tissue using histological images. A total of 3,000 hematoxylin and eosin (H&E) stained pathological images were collected for both normal pancreatic tissue and pancreatic cancer tissue. The images were acquired using a microscopic slide scanning system in our laboratory. After preprocessing steps such as cropping, resizing, and normalization, the images were input into two deep neural networks, ResNet and VGG, for training and testing. The deep learning models were implemented using the PyTorch framework and tested on a CUDA10 parallel computing platform. ResNet achieved an accuracy of 92.27% and an F1-score of 0.92, outperforming VGG, which achieved an accuracy of 86.01% and an F1-score of 0.86. K-fold cross-validation was performed to evaluate the generalization ability of the models. The results showed that deep learning models, particularly ResNet, offer substantial promise for improving the accuracy of pancreatic cancer diagnosis, potentially facilitating earlier and more accurate detection in clinical settings.