Exploring the Application of Deep Learning in Lung Cancer Prediction

DOI

10.2991/978-94-6463-540-9_13How to use a DOI?

Keywords

Artificial intelligence; Lung cancer prediction; Deep learning; Convolutional neural network

Abstract

Deep learning is a branch of artificial intelligence. Deep learning can make learning predictions by simulating the working principles between neurons in the human brain. Lung cancer is a major problem in the medical field, and the prediction of lung cancer based on deep learning is of great significance. Therefore, this article will discuss and study the current status of lung cancer and how deep learning predicts it. This article concludes that deep learning is more effective than other methods for complex diseases such as lung cancer. Among them, convolutional neural networks can have higher accuracy. The emergence of deep learning makes complex medical content simple to quantify. Make it easier for doctors to judge treatment.The prediction and research of cancer through deep learning can improve the treatment effect and benefit mankind. This article explores the help and contribution of deep learning in the field of lung cancer through the characteristics of deep learning.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jie Cheng
PY  - 2024
DA  - 2024/10/16
TI  - Exploring the Application of Deep Learning in Lung Cancer Prediction
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 110
EP  - 116
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_13
DO  - 10.2991/978-94-6463-540-9_13
ID  - Cheng2024
ER  -