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For: Ali S, Li J, Pei Y, Khurram R, Rehman KU, Mahmood T. A Comprehensive Survey on Brain Tumor Diagnosis Using Deep Learning and Emerging Hybrid Techniques with Multi-modal MR Image. Arch Computat Methods Eng. [DOI: 10.1007/s11831-022-09758-z] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Li J, Di Y, Qi G, Zhao L, Ju F. Attention-based Multi-flow Network for COVID-19 Classification and Lesion Localization from Chest CT. 2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) 2022. [DOI: 10.1109/icnsc55942.2022.10004191] [Reference Citation Analysis]
2 Zahid U, Ashraf I, Khan MA, Alhaisoni M, Yahya KM, Hussein HS, Alshazly H, Javed AR. BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification. Computational Intelligence and Neuroscience 2022;2022:1-13. [DOI: 10.1155/2022/1465173] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
3 Xie Y, Zaccagna F, Rundo L, Testa C, Agati R, Lodi R, Manners DN, Tonon C. Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives. Diagnostics 2022;12:1850. [DOI: 10.3390/diagnostics12081850] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]