Study on the Effect of Depth on Convolutional Neural Networks for Brain Tumor Detection

DOI

10.2991/978-94-6463-040-4_48How to use a DOI?

Keywords

Brain tumor detection; convolutional neural network; Influence of depth

Abstract

Brain tumors are one of the invasive diseases in children and adults. Due to the complexity involved in brain tumors and their characteristics, manual examination may be error prone. The use of automatic classification technology using machine learning (ML) has always shown higher accuracy than manual classification [1]. Convolutional Neural Network (CNN) is widely used as a deep learning algorithm in detecting brain tumor. However, many people have not paid their attention to how the number of layers affect the accuracy of CNN when applying it in detecting tumor. To find the relationship between the number of layers of CNN model detecting the brain tumor and the accuracy of the model, this study implements three different models with different number of layers. By using a certain data set and preprocessing, the three models’ codes are successfully run, and figures and output of the accuracy and loss are showed up. After comparing the graphs and output, a certain relationship is concluded. In this article, it is indicated that the number of layers of model has positive relationship with the accuracy of this model: the more layers the model has, the higher accuracy this model will provide.

Copyright

© 2023 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  - Yuxuan Li
PY  - 2022
DA  - 2022/12/27
TI  - Study on the Effect of Depth on Convolutional Neural Networks for Brain Tumor Detection
BT  - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
PB  - Atlantis Press
SP  - 316
EP  - 321
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-040-4_48
DO  - 10.2991/978-94-6463-040-4_48
ID  - Li2022
ER  -