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Brain tumor classification for MR images using transfer learning and fine-tuning
Abstract
Accurate and precise brain tumor MR images classification plays important role in clinical diagnosis and decision making for patient treatment. The key challenge in MR images classification is the semantic gap between the low-level visual information captured by the MRI machine and the high-level information perceived by the human evaluator. The traditional machine learning techniques for classification focus only on low-level or high-level features, use some handcrafted features to reduce this gap and require good feature extraction and classification methods. Recent development on deep learning has shown great progress and deep convolution neural networks (CNNs) have succeeded in the images classification task. Deep learning is very powerful for feature representation that can depict low-level and high-level information completely and embed the phase of feature extraction and classification into self-learning but require large training dataset in general. For most of the medical imaging scenario, the training datasets are small, therefore, it is a challenging task to apply the deep learning and train CNN from scratch on the small dataset. Aiming this problem, we use pre-trained deep CNN model and propose a block-wise fine-tuning strategy based on transfer learning. The proposed method is evaluated on T1-weighted contrast-enhanced magnetic resonance images (CE-MRI) benchmark dataset. Our method is more generic as it does not use any handcrafted features, requires minimal preprocessing and can achieve average accuracy of 94.82% under five-fold cross-validation. We compare our results not only with the traditional machine learning but also with deep learning methods using CNNs. Experimental results show that our proposed method outperforms state-of-the-art classification on the CE-MRI dataset.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Research works printed but not linked: 22
- Avni, 2011, X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words, IEEE Trans. Med. Imaging, 30, 733, 10.1109/TMI.2010.2095026 · DOI · Paper page
- Bengio, 2009, Learning deep architectures for AI, Found. Trends® Mach. Learn., 2, 1, 10.1561/2200000006 · DOI · Paper page
- Chen, 2015, Standard plane localization in fetal ultrasound via domain transferred deep neural networks, IEEE J. Biomed. Health Inform., 19, 1627, 10.1109/JBHI.2015.2425041 · DOI · Paper page
- Chen, 2016, Deep contextual networks for neuronal structure segmentation, AAAI, 1167
- Cheng, 2017, Brain tumor dataset, figshare. Dataset.
- Cheng, 2016, Retrieval of brain tumors by adaptive spatial pooling and Fisher vector representation, PLoS One, 11
- Gao, 2016, Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks, Comput. Methods Biomech. Biomed. Eng. Imaging Vis., 1
- Greenspan, 2016, Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique, IEEE Trans. Med. Imaging, 35, 1153, 10.1109/TMI.2016.2553401 · DOI · Paper page
- Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, 313, 504, 10.1126/science.1127647 · DOI · Paper page
- Javed, 2013, MRI brain classification using texture features, fuzzy weighting and support vector machine, Prog. Electromagn. Res. B, 53, 73, 10.2528/PIERB13052805 · DOI
- Jiang, 2013, 3D brain tumor segmentation in multimodal MR images based on learning population-and patient-specific feature sets, Comput. Med. Imaging Graph., 37, 512, 10.1016/j.compmedimag.2013.05.007 · DOI
- John, 2012, Brain tumor classification using wavelet and texture based neural network, Int. J. Sci. Eng. Res., 3, 1
- Margeta, 2017, Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition, Comput. Methods Biomech. Biomed. Eng. Imaging Vis., 5, 339, 10.1080/21681163.2015.1061448 · DOI
- Paul, 2017, Deep learning for brain tumor classification, medical imaging 2017: biomedical applications in molecular, structural, and functional imaging, International Society for Optics and Photonics, p.
- Selvaraj, 2007, Brain MRI slices classification using least squares support vector machine, Int. J. Intell. Comput. Med. Sci. Image Process., 1, 21
- Simonyan, 2014, Very deep convolutional networks for large-scale image recognition, arXiv preprint
- Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929
- Suk, 2016, State-space model with deep learning for functional dynamics estimation in resting-state fMRI, NeuroImage, 129, 292, 10.1016/j.neuroimage.2016.01.005 · DOI · Paper page
- Szegedy, 2013, Deep neural networks for object detection, Adv. Neural Inf. Process. Syst., 2553
- Yang, 2012, Content‐based retrieval of brain tumor in contrast‐enhanced MRI images using tumor margin information and learned distance metric, Med. Phys., 39, 6929, 10.1118/1.4754305 · DOI
- Yang, 2012, Content-based retrieval of focal liver lesions using bag-of-visual-words representations of single-and multiphase contrast-enhanced CT images, J. Digit. Imaging, 25, 708, 10.1007/s10278-012-9495-1 · DOI
- Zhang, 2015, Deep neural networks in machine translation: an overview, IEEE Intell. Syst., 30, 16, 10.1109/MIS.2015.69 · DOI · Paper page
Cited by
Domain adaptation for medical image analysis: A survey
Guan, H., & Liu, M. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3117407 · Cited by 1,057
Transfer learning for medical image classification: a literature review
Kim, H. E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M. E., & Ganslandt, T. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00793-7 · Cited by 965
Accurate brain tumor detection using deep convolutional neural network
Khan, M. S. I., Rahman, A., Debnath, T., Karim, M. R., Nasir, M. K., Band, S. S., Mosavi, A., & Dehzangi, I. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.039 · Cited by 401
Physical energy and data-driven models in building energy prediction: A review
Chen, Y., Guo, M., Chen, Z., Chen, Z., & Ji, Y. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.01.162 · Cited by 384
Transfer learning techniques for medical image analysis: A review
Kora, P., Ooi, C. P., Faust, O., Raghavendra, U., Gudigar, A., Chan, W. Y., Meenakshi, K., Swaraja, K., Plawiak, P., & Rajendra Acharya, U. (2022) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2021.11.004 · Cited by 367
Metadata
- Authors
- Swati, Z. N. K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., & Lu, J.
- Venue
- Computerized Medical Imaging and Graphics
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 75
- Pages
- 34-46
- ISSN
- 0730-4862 (ISSN-L)
- 0895-6111
- OpenAlex
- W2945839551
- Cited by
- 840
- References
- 24
- FWCI
- 27.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Funders
- National Key Research and Development Program of China (2018YFB1004)
- 111 Project (B13022)
- Natural Science Foundation of Jiangsu Province of China (20131351)
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- Swati, Z. N. K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., & Lu, J. (2019). Brain tumor classification for MR images using transfer learning and fine-tuning. Computerized Medical Imaging and Graphics. https://doi.org/10.1016/j.compmedimag.2019.05.001
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