TY - GEN
T1 - A Hybrid Deep Model for Brain Tumor Classification
AU - Ayaz, Hamail
AU - Ahmad, Muhammad
AU - Tormey, David
AU - McLoughlin, Ian
AU - Unnikrishnan, Saritha
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Classification of brain tumors from Magnetic Resonance Images (MRIs) using Computer-Aided Diagnosis (CAD) has faced some major challenges. Diagnosis of brain tumors such as glioma, meningioma, and pituitary mostly rely on manual evaluation by neuro-radiologists and is prone to human error and subjectivity. In recent years, Machine Learning (ML) techniques have been used to improve the accuracy of tumor diagnosis with the expense of intensive pre-processing and computational cost. Therefore, this work proposed a hybrid Convolutional Neural Network (CNN) (i.e., AlexNet followed by SqueezeNet) to extract quality tumor biomarkers for better performance of the CAD system using brain tumor MRI’s. The features extracted using AlexNet and SqueezeNet are fused to preserve the most important biomarkers in a computationally efficient manner. A total of 3064 brain tumors (708 Meningioma, 1426 Glioma, and 930 Pituitaries) MRIs have been experimented. The proposed model is evaluated using several well-known metrics, i.e., Overall accuracy (94%), Precision (92%), Recall (95%), and F1 score (93%) and outperformed many state of the art hybrid methods.
AB - Classification of brain tumors from Magnetic Resonance Images (MRIs) using Computer-Aided Diagnosis (CAD) has faced some major challenges. Diagnosis of brain tumors such as glioma, meningioma, and pituitary mostly rely on manual evaluation by neuro-radiologists and is prone to human error and subjectivity. In recent years, Machine Learning (ML) techniques have been used to improve the accuracy of tumor diagnosis with the expense of intensive pre-processing and computational cost. Therefore, this work proposed a hybrid Convolutional Neural Network (CNN) (i.e., AlexNet followed by SqueezeNet) to extract quality tumor biomarkers for better performance of the CAD system using brain tumor MRI’s. The features extracted using AlexNet and SqueezeNet are fused to preserve the most important biomarkers in a computationally efficient manner. A total of 3064 brain tumors (708 Meningioma, 1426 Glioma, and 930 Pituitaries) MRIs have been experimented. The proposed model is evaluated using several well-known metrics, i.e., Overall accuracy (94%), Precision (92%), Recall (95%), and F1 score (93%) and outperformed many state of the art hybrid methods.
KW - Brain tumor
KW - Classification
KW - Ensemble learning
KW - Hybrid model
UR - http://www.scopus.com/inward/record.url?scp=85115127327&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-3880-0_29
DO - 10.1007/978-981-16-3880-0_29
M3 - Conference contribution
AN - SCOPUS:85115127327
SN - 9789811638794
T3 - Lecture Notes in Electrical Engineering
SP - 282
EP - 291
BT - Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis
A2 - Su, Ruidan
A2 - Zhang, Yu-Dong
A2 - Liu, Han
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021
Y2 - 25 March 2021 through 26 March 2021
ER -