Brain Tumors Classification for MR images based on Attention Guided Deep Learning Model

Magnetic Resonance Imaging (MRI) technology has been widely applied to generate high-resolution images for brain tumor diagnosis. However, manual image reading is very time and labor consuming. Instead, automatic tumor detection based on deep learning models has emerged recently. Although existing m...

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Bibliographic Details
Main Authors: Zhang, Yuhao, Wang, Shuhang, Wu, Haoxiang, Hu, Kejia, Ji, Shufan
Format: Conference Proceeding
Language:English
Subjects:
Attention
Biological system modeling
Brain - diagnostic imaging
Brain modeling
Brain Neoplasms - diagnostic imaging
Convolution
Convolutional neural networks
Deep Learning
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Manuals
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Summary:Magnetic Resonance Imaging (MRI) technology has been widely applied to generate high-resolution images for brain tumor diagnosis. However, manual image reading is very time and labor consuming. Instead, automatic tumor detection based on deep learning models has emerged recently. Although existing models could well detect brain tumors from MR images, they seldom distinguished primary intracranial tumors from secondary ones. Therefore, in this paper, we propose an attention guided deep Convolution Neural Network (CNN) model for brain tumor diagnosis. Experimental results show that our model could effectively detect tumors from brain MR images with 99.18% average accuracy, and distinguish the primary and secondary intracranial tumors with 83.38% average accuracy, both under ten-fold cross-validation. Our model, outperforming existing works, is competitive to medical experts on brain tumor diagnosis.
ISSN:2694-0604
DOI:10.1109/EMBC46164.2021.9630571