APA (7th ed.) Citation

ŞİŞİK, F., & SERT, E. (2020). Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware. Medical hypotheses, 136, 109507. https://doi.org/10.1016/j.mehy.2019.109507

Chicago Style (17th ed.) Citation

ŞİŞİK, Fatih, and Eser SERT. "Brain Tumor Segmentation Approach Based on the Extreme Learning Machine and Significantly Fast and Robust Fuzzy C-means Clustering Algorithms Running on Raspberry Pi Hardware." Medical Hypotheses 136 (2020): 109507. https://doi.org/10.1016/j.mehy.2019.109507.

MLA (9th ed.) Citation

ŞİŞİK, Fatih, and Eser SERT. "Brain Tumor Segmentation Approach Based on the Extreme Learning Machine and Significantly Fast and Robust Fuzzy C-means Clustering Algorithms Running on Raspberry Pi Hardware." Medical Hypotheses, vol. 136, 2020, p. 109507, https://doi.org/10.1016/j.mehy.2019.109507.