Deep-Stacked Convolutional Neural Networks for Brain Abnormality Classification Based on MRI Images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology
Link
https://link.springer.com/content/pdf/10.1007/s10278-023-00828-7.pdf
Reference54 articles.
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2. Siddiqui, M. F., Mujtaba, G., Reza, A. W., & Shuib, L. (2017). Multi-class disease classification in brain MRIs using a computer-aided diagnostic system. Symmetry, 9(3), 1–14. https://doi.org/10.3390/sym9030037
3. Zhang, Y. D., Dong, Z., Wang, S. H., Yu, X., Yao, X., Zhou, Q., Hu, H., Li, M., Jiménez-Mesa, C., Ramirez, J., Martinez, F. J., & Gorriz, J. M. (2020). Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. Information Fusion, 64, 149–187. https://doi.org/10.1016/J.INFFUS.2020.07.006
4. El-Dahshan, E. A. S., Mohsen, H. M., Revett, K., & Salem, A. B. M. (2014). Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Systems with Applications, 41(11), 5526–5545. https://doi.org/10.1016/j.eswa.2014.01.021
5. Nayak, D. R., Dash, R., & Majhi, B. (2016). Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing, 177, 188–197. https://doi.org/10.1016/j.neucom.2015.11.034
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