Anticipating brain tumor classification and severity levels: employing the CDF-BILSTM model approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Link
https://link.springer.com/content/pdf/10.1007/s11082-023-05760-2.pdf
Reference20 articles.
1. Abdelaziz Ismael, S.A., Mohammed, A., Hefny, H.: An enhanced deep learning approach for brain cancer MRI images classification using residual networks. Artif. Intell. Med. 102, 1–14 (2020). https://doi.org/10.1016/j.artmed.2019.101779
2. Ahuja, S., Panigrahi, B.K., Gandhi, T.K.: Enhanced performance of Dark-Nets for brain tumor classification and segmentation using colormap-based superpixel techniques. Mach. Learn. Appl. 7, 1–13 (2022). https://doi.org/10.1016/j.mlwa.2021.100212
3. Arabahmadi, M., Farahbakhsh, R., Rezazadeh, J.: Deep learning for smart healthcare—a survey on brain tumor detection from medical imaging. Sensors 22(5), 1–27 (2022). MDPI. https://doi.org/10.3390/s22051960
4. Ayadi, W., Elhamzi, W., Charfi, I., Atri, M.: Deep CNN for brain tumor classification. Neural. Process. Lett. 53(1), 671–700 (2021). https://doi.org/10.1007/s11063-020-10398-2
5. Chattopadhyay, A., Maitra, M.: MRI-based brain tumour image detection using CNN based deep learning method. Neurosci. Informat. 2(4), 1–6 (2022). https://doi.org/10.1016/j.neuri.2022.100060
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