Advancements in Pancreatic Cancer Detection: A Comprehensive Investigation of Convolutional Neural Network Applications

Wenhan Wang

2024

Abstract

Pancreatic cancer is one of the leading causes of cancer death and poses a major challenge to the current health care system due to its early concealment and high mortality. At the same time, the excellent performance of machine learning technology, especially convolutional neural network technology in the fields of medical image target detection and semantic segmentation provides a new solution for the recognition and early prevention of pancreatic cancer. This review introduces the application of Convolutional Neural Networks (CNNs) in the field of pancreatic cancer detection in recent years, introduces the basic structure and function of CNN model, and further introduces the characteristics of classical CNN models such as ResNet and DenseNet and their applications in the field of pancreatic cancer detection. Three complex CNN models, PANDA, YCNN and DACTransNet, are emphasized, and their structures, characteristics and applications in pancreatic cancer detection are introduced. These models leverage CNN's ability to extract complex features from medical images, facilitating precise tumor identification. Then, the user-friendliness and interpretability of different models are discussed, and the lack of clinical evaluation in current studies is pointed out. Future research may focus on improving the CNN architecture, enhancing model generalization, and addressing interpretability issues to optimize clinical applications. This review provides insight into the current state and prospects of CNN-based pancreatic cancer detection and outlines possible directions for future exploration.

Paper Citation

in Harvard Style

Wang W. (2024). Advancements in Pancreatic Cancer Detection: A Comprehensive Investigation of Convolutional Neural Network Applications. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 665-668. DOI: 10.5220/0012961500004508

in Bibtex Style

@conference{emiti24,
author={Wenhan Wang},
title={Advancements in Pancreatic Cancer Detection: A Comprehensive Investigation of Convolutional Neural Network Applications},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={665-668},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012961500004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Advancements in Pancreatic Cancer Detection: A Comprehensive Investigation of Convolutional Neural Network Applications
SN - 978-989-758-713-9
AU - Wang W.
PY - 2024
SP - 665
EP - 668
DO - 10.5220/0012961500004508
PB - SciTePress