An Optimized Deep Learning Model with Feature Fusion for Brain Tumor Detection
The automatic detection of brain tumor from large volumes of MRI images using deep learning is a issue that necessitates substantial computing resources. So,in this study, a brain tumor detection framework using feature fusion from optimized shallow and deep learning models is proposed that efficiently detects different types of tumors. The human brain is a 3D object and the intensity of abnormal tissue varies as per location. As a result, incorporating surrounding tissue into tumor region can help to discriminate between the type of tumor and its severity. To extract deep characteristics from tumor region and adjacent tissues, deep models such as Inception-V3 is employed using transfer learning. Deep features are especially important in tumour detection, however as the network size grows, certain low-level insights about tumor are lost. As a result, a novel optimized shallow model is designed to extract low-level features. To overcome this limitation of information loss, deep and shallow features are fused. Our extensive simulation and experiment done on a publicly available benchmark dataset shows that an optimized hybrid deep learning model with ROI expansion improves tumor detection accuracy by 9\%. These findings support the theory that the tissues adjacent to the tumor contain unique information and feature fusion compensates for information loss.
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- Ardhendu, S. and et al. 2022. Brain tumor classification using fine-tuned google net features and machine learning algorithms: Iomt enabled cad system. IEEE Journal of Biomedical and Health Informatics Vol.26, pp.983–991. DOI: https://doi.org/10.1109/JBHI.2021.3100758
- C. ¨Oks¨uz, O. Urhan, M. G. 2021. Brain tumor classification using the fused features extracted from expanded tumor region. Biomedical Signal Processing and Control Vol.72, pp.1–15. DOI: https://doi.org/10.1016/j.bspc.2021.103356
- Cheng, J. 2017. https://figshare.com/articles/dataset/braintumordataset/1512427/5, accessed2022. Deepak, S. and et al. 2019. Brain tumor classification using deep cnn features via transfer learning. Computers in Biology and Medicine Vol.111. DOI: https://doi.org/10.1016/j.compbiomed.2019.103345
- Ghassemi, N. and et al. 2020. Deep neural network with generative adversarial networks pretraining for brain tumor classification based on mr images. Biomedical Signal Processing and Control Vol.57, pp.34–46. DOI: https://doi.org/10.1016/j.bspc.2019.101678
- H. Ramchand., P. Suraj, A. K. D. 2022. Improved salp swarm optimization-based fuzzy centroidregion growing for liver tumor segmentation and deep learning oriented classification. International Journal of Next-Generation Computing Vol.5, pp.5.
- H. Selvaraj, S.T. Selvi, D. S. L. G. 2007. Brain mri slices classification using least squares support vector machine. nt. J. Intell. Comput. Med. Sci. Image Process. Vol.1, pp.21–33. DOI: https://doi.org/10.1080/1931308X.2007.10644134
- J. Jiang, Y. W. and et al. 2013. 3d brain tumor segmentation in multimodal mr images based on learning population- and patient-specific feature sets. Computerized Medical Imaging and Graphics Vol.37, pp.512–521. DOI: https://doi.org/10.1016/j.compmedimag.2013.05.007
- J. Sachdeva, V. K. and et al. 2011. Multiclass brain tumor classification using ga-svm. IEEE, pp.182–189. DOI: https://doi.org/10.1109/DeSE.2011.31
- John, P. 2012. Brain tumor classification using wavelet and texture based neural network. IJSER.
- LF. Yan, YZ. Sun, S. Z. and et al. 2019. Perfusion, diffusion, or brain tumor barrier integrity: Which represents the glioma features best. Cognitive Systems Research Vol.2019:11, pp.9989—10000. DOI: https://doi.org/10.2147/CMAR.S197839
- Milica, B. and et al. 2020. Classification of brain tumors from mri images using a convolutional neural network. Applied Sciences Vol.10. DOI: https://doi.org/10.3390/app10061999
- N.K. El Abbadi, N. K. 2017. Brain cancer classification based on features and artificial neural network. IJARCCE Vol.6, pp.123–134. DOI: https://doi.org/10.17148/IJARCCE.2017.6125
- P. Afshar, KN. Plataniotis, A. M. 2019. Capsule networks for brain tumor classification based on mri images and coarse tumor boundaries. IEEE. DOI: https://doi.org/10.1109/ICASSP.2019.8683759
- Pashaei, A. and et al. 2018. Brain tumor classification via convolutional neural network and extreme learning machines. IEEE. DOI: https://doi.org/10.1109/ICCKE.2018.8566571
- Q. Dou, H. Chen, L. Y. L. J. Q. D. W. 2016. Automatic detection of cerebral microbleeds from mr images via 3d convolutional neural networks. IEEE Transactions on Medical Imaging Vol.35, 11, pp.1182–1195. DOI: https://doi.org/10.1109/TMI.2016.2528129
- S. Iqbal, MUG. Khan, L. Z. and et al. 2017. A. computer-assisted brain tumor type discriminationusing magnetic resonance imaging features. Biomedical Engineering Letters Vol.8. DOI: https://doi.org/10.1007/s13534-017-0050-3
- Swati ZNK, e. a. 2021. Brain tumor classification for mr images using transfer learning and fine-tuning. Expert System with Application, Elsevier Vol.180.
- Ujjwal, B. and et al. 2020. A novel approach for fully automatic intra-tumor segmentation with 3d u-net architecture for gliomas. Frontiers in Computational Neuroscience Vol.14. DOI: https://doi.org/10.3389/fncom.2020.00010