Abstract
Brain tumors are the most destructive disease, leading to a very short life expectancy in their highest grade. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce chance of survival of patients. The accurate diagnosis of brain tumor is a key point to make a proper treatment planning to cure and improve the existence of patients with brain tumors disease. The computer-aided tumor detection systems and convolutional neural networks provided success stories and have made important strides in the field of machine learning. The deep convolutional layers extract important and robust features automatically from the input space as compared to traditional predecessor neural network layers. In the proposed framework, we conduct three studies using three architectures of convolutional neural networks (AlexNet, GoogLeNet, and VGGNet) to classify brain tumors such as meningioma, glioma, and pituitary. Each study then explores the transfer learning techniques, i.e., fine-tune and freeze using MRI slices of brain tumor dataset—Figshare. The data augmentation techniques are applied to the MRI slices for generalization of results, increasing the dataset samples and reducing the chance of over-fitting. In the proposed studies, the fine-tune VGG16 architecture attained highest accuracy up to 98.69 in terms of classification and detection.
Similar content being viewed by others
References
T.A. Abir, J.A. Siraji, E. Ahmed, B. Khulna, Analysis of a novel MRI based brain tumour classification using probabilistic neural network (PNN). Int. J. Sci. Res. Sci. Eng. Technol. 4(8), 65–79 (2018)
N. Abiwinanda, M. Hanif, S.T. Hesaputra, A. Handayani, T.R. Mengko, Brain tumor classification using convolutional neural network. In World Congress on Medical Physics and Biomedical Engineering, pp. 183–189. Springer (2019)
P. Afshar, A. Mohammadi, K.N Plataniotis, Brain tumor type classification via capsule networks. arXiv preprint: arXiv:1802.10200 (2018)
J. Cheng, Brain tumor dataset. figshare. dataset. https://doi.org/10.6084/m9.figshare.1512427.v5. Accessed 30 May 2018
J. Cheng, W. Yang, M. Huang, W. Huang, J. Jiang, Y. Zhou, R. Yang, J. Zhao, Y. Feng, Q. Feng, Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PLoS ONE 11(6), e0157112 (2016)
J. Cheng, W. Huang, R. Shuangliang Cao, W.Y. Yang, Z. Yun, Z. Wang, Q. Feng, Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE 10(10), e0140381 (2015)
J. Deng, W.Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, ImageNet: a large-scale hierarchical image database. In IEEE Conference on Computer Vision and Pattern Recognition, 2009 CVPR 2009, pp. 248–255. IEEE (2009)
M.R. Ismael, I. Abdel-Qader, Brain tumor classification via statistical features and back-propagation neural network. In 2018 IEEE International Conference on Electro/Information Technology (EIT), pp. 0252–0257. IEEE (2018)
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
A. Naseer, M. Rani, S. Naz, M.I. Razzak, M. Imran, G. Xu, Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04069-0
S. Naz, A.I. Umar, R. Ahmad, I. Siddiqi, S.B. Ahmed, M.I. Razzak, F. Shafait, Urdu Nastaliq recognition using convolutional–recursive deep learning. Neurocomputing 243, 80–87 (2017)
I. Razzak, M. Imran, G. Xu, Efficient brain tumor segmentation with multiscaleancer statistics two-pathway-group conventional neural networks. IEEE J. Biomed. Health Inf. (2018). https://doi.org/10.1109/JBHI.2018.2874033
M.I. Razzak, Malarial parasite classification using recurrent neural network. Int. J. Image Process. 9, 69 (2015)
M.I. Razzak, B. Alhaqbani, Automatic detection of malarial parasite using microscopic blood images. J. Med. Imaging Health Inform. 5(3), 591–598 (2015)
M.I. Razzak, M. Imran, G. Xu, Big data analytics for preventive medicine. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04095-y
M.I. Razzak, S. Naz, Microscopic blood smear segmentation and classification using deep contour aware CNN and extreme machine learning. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 801–807. IEEE (2017)
M.I. Razzak, S. Naz, A. Zaib, Deep learning for medical image processing: overview, challenges and the future. In Classification in BioApps, pp. 323–350. Springer (2018)
A. Rehman, S. Naz, M.I. Razzak, H.A. Ibrahim, Automatic visual features for writer identification: a deep learning approach. IEEE Access 7, 17149–17157 (2019)
A. Rehman, S. Naz, M.I. Razzak, Writer identification using machine learning approaches: a comprehensive review. Multimed. Tools Appl. 78(8), 10889–10931 (2019)
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
S.H. Shirazi, A.I. Umar, S. Naz, M.I. Razzak, Efficient leukocyte segmentation and recognition in peripheral blood image. Technol. Health Care 24(3), 335–347 (2016)
R. Siegel, C.R. Miller, A. Jamal, Cancer statistics, 2017. CA Cancer J. Clin. 67(1), 7–30 (2017)
R.L. Siegel, K.D. Miller, A. Jemal, Cancer statistics, 2015. CA Cancer J. Clin. 65(1), 5–29 (2015)
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint: arXiv:1409.1556 (2014)
C. Szegedy, W.Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
W. Widhiarso, Y. Yohannes, C. Prakarsah, Brain tumor classification using gray level co-occurrence matrix and convolutional neural network. IJEIS (Indones. J. Electron. Instrum. Syst.) 8(2), 179–190 (2018)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Imran’s work is supported by the Deanship of Scientific Research, King Saud University through research group Project Number RG-1435-051.
Rights and permissions
About this article
Cite this article
Rehman, A., Naz, S., Razzak, M.I. et al. A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning. Circuits Syst Signal Process 39, 757–775 (2020). https://doi.org/10.1007/s00034-019-01246-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00034-019-01246-3