A survey on brain tumor image analysis
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11517-023-02873-4.pdf
Reference80 articles.
1. Abd-Ellah MK, Awad AI, Khalaf AA, Hamed HF (2019) A review on brain tumor diagnosis from MRI images: practical implications and key achievements and lessons learned. Magn Reson Imaging 61:300–318
2. Aboelenein NM, Songhao P, Koubaa A, Noor A, Afifi A (2020) Httu-Net: hybrid two track U-Net for automatic brain tumor segmentation. IEEE Access 8:101406–101415
3. Al-Galal SAY, Alshaikhli IFT, Abdulrazzaq MM (2021) MRI brain tumor medical images analysis using deep learning techniques: a systematic review. Health Technol 11(7):1–16
4. Alipour N, Hasanzadeh RP (2021) Superpixel-based brain tumor segmentation in MR images using an extended local fuzzy active contour model. Multimed Tools Appl 80(6):8835–8859
5. Angulakshmi M, Priya GL (2019) Walsh Hadamard transform for simple linear iterative clustering (SLIC) superpixel based spectral clustering of multimodal MRI brain tumor segmentation. Irbm 40(5):253–262
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