The Advancements and Applications of Artificial Intelligence in Gastric Cancer Diagnosis

Zilong Xu

2024

Abstract

Intelligence (AI)-enhanced diagnosis, offering faster and cost-effective solutions through advanced imaging and data analysis. This study aims to provide a comprehensive review of the detection of gastric cancer by AI. The research methods mainly include machine learning and deep learning aspects, covering the Convolutional Neural Network (CNN) and random forest and other methods. In terms of traditional machine learning methods, this paper detailed the application of random forest and Support Vector Machine (SVM) in the detection of gastric cancer. Random forest is used to predict patient survival status, improving the generalization ability of the algorithm by weighting methods. The SVM is used to identify Microsatellite Instability (MSI) and Lymph Node Metastasis (LNM) to provide doctors with important information to guide treatment decisions. In terms of deep learning methods, this paper focused on the application of CNNs for gastric cancer detection. The research team developed a model for the detection and depth prediction of Early Gastric Cancer (EGC), which improved the detection accuracy of EGC by segmenting endoscopic images and classifying them using the VGG-16 model. The discussion section discusses in detail the shortcomings of AI models in gastric cancer detection, such as poor interpretability, insufficient data diversity, difficulties with physician and model coordination, and privacy and ethical issues. Relevant suggestions are made, including injecting more domain knowledge, enhancing data diversity, optimizing real-time models, enhancing collaboration between doctors and models, and adopting privacy protection technologies.

Paper Citation

in Harvard Style

Xu Z. (2024). The Advancements and Applications of Artificial Intelligence in Gastric Cancer Diagnosis. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 339-343. DOI: 10.5220/0012838200004547

in Bibtex Style

@conference{icdse24,
author={Zilong Xu},
title={The Advancements and Applications of Artificial Intelligence in Gastric Cancer Diagnosis},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={339-343},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012838200004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - The Advancements and Applications of Artificial Intelligence in Gastric Cancer Diagnosis
SN - 978-989-758-690-3
AU - Xu Z.
PY - 2024
SP - 339
EP - 343
DO - 10.5220/0012838200004547
PB - SciTePress