TY - GEN
T1 - Brain Tumors Classification for MR images based on Attention Guided Deep Learning Model
AU - Zhang, Yuhao
AU - Wang, Shuhang
AU - Wu, Haoxiang
AU - Hu, Kejia
AU - Ji, Shufan
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85122536051
U2 - 10.1109/EMBC46164.2021.9630571
DO - 10.1109/EMBC46164.2021.9630571
M3 - 会议稿件
C2 - 34891930
AN - SCOPUS:85122536051
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3233
EP - 3236
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -