Advanced Brain Tumor Detection Using Enhanced Visual Information Processing

DOI

10.2991/978-94-6463-512-6_76How to use a DOI?

Keywords

Brain Tumor Detection; Convolutional Neural Networks (CNNs); Early Stopping Method

Abstract

In the fields of computer science, artificial intelligence, and medical science, brain tumor detection is quite important. This paper proposes a strategy utilizing Convolutional Neural Networks (CNNs) to build a detection model. CNNs, by simulating the human brain's processing of visual information, can automatically learn advanced features from images and accomplish tasks such as image recognition and classification. The goal of this paper is to utilize basic and straightforward methods to achieve accurate brain tumor detection. Additionally, the paper compares the influence of the early stopping method and thresholding approach on the proposed model. The outcomes reflect the influence of 2 methods is similar. The test accuracy for the model with the early stopping method is approximately 97.5%, while the model employing the thresholding approach achieves a test accuracy of about 98%. Brain tumor detection using CNNs signifies a significant advancement in computer science and artificial intelligence, with immense practical significance in the medical field and for patient well-being. By leveraging the power of automation, CNNs aid radiologists in detecting tumors with greater speed and precision, paving the way for earlier diagnosis and therapeutic interventions. This, in turn, can significantly enhance patient outcomes, reduce the risk of complications, and potentially save lives.

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  - Xining Feng
PY  - 2024
DA  - 2024/09/23
TI  - Advanced Brain Tumor Detection Using Enhanced Visual Information Processing
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 721
EP  - 731
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_76
DO  - 10.2991/978-94-6463-512-6_76
ID  - Feng2024
ER  -