An Analysis of Transfer Learning Model for Deep Neural Network-based Automated Brain Tumor Diagnosis from MR Images

DOI

10.2991/978-94-6463-529-4_19How to use a DOI?

Keywords

transfer learning; ResNet50; magnetic resonance images; brain tumor; classification; fine-tuning

Abstract

A crucial stage in the categorization of brain tumors for medical diagnosis and treatment. Magnetic resonance imaging (MRI) is routinely used to distinguish brain tumors; however, manual interpretation of the MRI data is arbitrary and time-consuming. In this work, we used transfer learning and the ResNet50 model with fine-tuning to classify brain tumor MR scans data into three different categories: meningioma, glioma, and pituitary tumors. Once the images had been pre-processed to lower noise and boost quality, they were then normalized and resized. The improved ResNet50 model achieved accuracy, precision, and recall, of 95.9%, 95.7%, and 95.19%. The results highlight the value of these techniques for analyzing medical images and demonstrate the potential for transfer learning and fine-tuning for the categorization of brain tumor MR images. This proposed study sets the framework for future research into the development of more sophisticated and effective techniques for categorizing MRI images of brain tumors.

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  - Ashfaq Hussain
AU  - Chintha Sri Pothuraju
AU  - Saharul Alom Barlaskar
AU  - Rabul Hussain Laskar
PY  - 2024
DA  - 2024/10/04
TI  - An Analysis of Transfer Learning Model for Deep Neural Network-based Automated Brain Tumor Diagnosis from MR Images
BT  - Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)
PB  - Atlantis Press
SP  - 207
EP  - 217
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-529-4_19
DO  - 10.2991/978-94-6463-529-4_19
ID  - Hussain2024
ER  -