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Evaluating deep learning models for pancreatic cancer diagnosis
Daohong Li1,2, Hui He1,2, Jinxing Hu1,2
1Department of Pathology, Henan Provincial People's Hospital, Zhengzhou, 450003, P. R. China.
Clinical and Experimental Medicine
|February 26, 2026
Summary
Artificial intelligence (AI) models can accurately detect pancreatic cancer from tissue images. The ResNet deep learning model showed higher accuracy than VGG for improved early diagnosis.
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Pancreatic cancer is aggressive, making early detection crucial for survival.
- Histopathological image analysis is key for cancer diagnosis.
- Deep learning (DL) shows promise in medical image-based disease identification.
Purpose of the Study:
- To evaluate deep learning models for distinguishing pancreatic cancer from normal tissue.
- To compare the performance of Residual Neural Network (ResNet) and Visual Geometry Group Network (VGG) for this task.
Main Methods:
- Collected and preprocessed 3,000 H&E stained histopathological images of normal and cancerous pancreatic tissue.
- Trained and tested ResNet and VGG deep learning models using the PyTorch framework.
- Utilized K-fold cross-validation to assess model generalization.
Main Results:
- ResNet achieved 92.27% accuracy and a 0.92 F1-score.
- VGG achieved 86.01% accuracy and a 0.86 F1-score.
- ResNet demonstrated superior performance in differentiating pancreatic cancer tissues.
Conclusions:
- Deep learning models, especially ResNet, show significant potential for enhancing pancreatic cancer diagnosis accuracy.
- These AI tools could facilitate earlier and more precise detection in clinical settings.
- Further development of AI in histopathology may improve patient outcomes for pancreatic cancer.