Artificial Intelligence-Based Classification of Brain Tumors
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-6370-2_13
Reference11 articles.
1. Ahmad, I., Ullah, I., Khan, W.U., Ur Rehman, A., Adrees, M.S., Saleem, M.Q., Cheikhrouhou, O., Hamam, H., Shafiq, M.: Efficient algorithms for E-healthcare to solve multiobject fuse detection problem. J. Healthcare Eng. 2021, 1–16 (2021)
2. Amin, J., Sharif, M., Raza, M., Saba, T., Sial, R., Shad, S.A.: Brain tumor detection: a long short-term memory (LSTM)-based learning model. Neural Comput. Appl. 32, 15965–15973 (2020)
3. Amin, J., Sharif, M., Haldorai, A., Yasmin, M., Nayak, R.S.: Brain tumor detection and classification using machine learning: a comprehensive survey. Compl. Intell. Syst. 1–23 (2021)
4. Bulla, P., Anantha, L., Peram, S.: Deep neural networks with transfer learning model for brain tumors classification. Traitement Du Sig. 37(4), 593–601 (2020)
5. Dvořák, P., & Menze, B.: Structured prediction with convolutional neural networks for multimodal brain tumour segmentation. In: Medical Computer Vision: Algorithms for Big Data: International Workshop, MCV 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 5–9, 2015, Revised Selected Papers 18, pp. 59–71. Springer International Publishing (2015)
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