TY - JOUR
T1 - PBTNet
T2 - A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors
AU - Lu, Si Yuan
AU - Satapathy, Suresh Chandra
AU - Wang, Shui Hua
AU - Zhang, Yu Dong
N1 - Publisher Copyright:
© Copyright © 2021 Lu, Satapathy, Wang and Zhang.
PY - 2021/10/15
Y1 - 2021/10/15
N2 - Brain tumors are among the leading human killers. There are over 120 different types of brain tumors, but they mainly fall into two groups: primary brain tumors and metastatic brain tumors. Primary brain tumors develop from normal brain cells. Early and accurate detection of primary brain tumors is vital for the treatment of this disease. Magnetic resonance imaging is the most common method to diagnose brain diseases, but the manual interpretation of the images suffers from high inter-observer variance. In this paper, we presented a new computer-aided diagnosis system named PBTNet for detecting primary brain tumors in magnetic resonance images. A pre-trained ResNet-18 was selected as the backbone model in our PBTNet, but it was fine-tuned only for feature extraction. Then, three randomized neural networks, Schmidt neural network, random vector functional-link, and extreme learning machine served as the classifiers in the PBTNet, which were trained with the features and their labels. The final predictions of the PBTNet were generated by the ensemble of the outputs from the three classifiers. 5-fold cross-validation was employed to evaluate the classification performance of the PBTNet, and experimental results demonstrated that the proposed PBTNet was an effective tool for the diagnosis of primary brain tumors.
AB - Brain tumors are among the leading human killers. There are over 120 different types of brain tumors, but they mainly fall into two groups: primary brain tumors and metastatic brain tumors. Primary brain tumors develop from normal brain cells. Early and accurate detection of primary brain tumors is vital for the treatment of this disease. Magnetic resonance imaging is the most common method to diagnose brain diseases, but the manual interpretation of the images suffers from high inter-observer variance. In this paper, we presented a new computer-aided diagnosis system named PBTNet for detecting primary brain tumors in magnetic resonance images. A pre-trained ResNet-18 was selected as the backbone model in our PBTNet, but it was fine-tuned only for feature extraction. Then, three randomized neural networks, Schmidt neural network, random vector functional-link, and extreme learning machine served as the classifiers in the PBTNet, which were trained with the features and their labels. The final predictions of the PBTNet were generated by the ensemble of the outputs from the three classifiers. 5-fold cross-validation was employed to evaluate the classification performance of the PBTNet, and experimental results demonstrated that the proposed PBTNet was an effective tool for the diagnosis of primary brain tumors.
KW - brain cells
KW - computer-aided diagnosis
KW - convolutional neural network
KW - extreme learning machine
KW - magnetic resonance imaging
KW - primary brain tumors
UR - http://www.scopus.com/inward/record.url?scp=85118306409&partnerID=8YFLogxK
U2 - 10.3389/fcell.2021.765654
DO - 10.3389/fcell.2021.765654
M3 - Article
AN - SCOPUS:85118306409
SN - 2296-634X
VL - 9
JO - Frontiers in Cell and Developmental Biology
JF - Frontiers in Cell and Developmental Biology
M1 - 765654
ER -