A brain tumor MRI image classification method based on deep DenseNet network

Xiaosong He; Kai Ma; Jie Huang; Hongjing He

doi:10.1117/12.3035103

19 July 2024 A brain tumor MRI image classification method based on deep DenseNet network

Xiaosong He, Kai Ma, Jie Huang, Hongjing He

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132130N (2024) https://doi.org/10.1117/12.3035103
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Brain tumor is a disease characterized by abnormal cell proliferation occurring within brain tissue or beneath the meninges, which may pose a serious threat to the health of patients. In medicine, the diagnosis of brain tumors is mainly based on magnetic resonance imaging (MRI) information. However, manually detecting lesions in brain tumor images is a tedious, time-consuming, and error prone task. Therefore, there is an urgent need for computer-aided methods with higher accuracy and efficiency to analyze brain tumors. Deep learning can quickly extract deep features of images and is widely used in the field of medical image classification and recognition. In this study, 7023 open source MRI images of brain tumors were obtained from the Kaggle database, including four types: glioma, meningioma, pituitary gland, and tumor free. Subsequently, a deep DenseNet network framework was introduced to construct a classification model. The results show that the DenseNet121 deep learning method has a model evaluation and validation accuracy of 98.32% on the test set. Finally, the visualization analysis of gradient weighted class activation maps provides interpretability for the detection results of deep learning models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaosong He, Kai Ma, Jie Huang, and Hongjing He "A brain tumor MRI image classification method based on deep DenseNet network", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132130N (19 July 2024); https://doi.org/10.1117/12.3035103

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available