Evaluating machine learning models for post-surgery treatment response assessment in glioblastoma multiforme: a comparative study of gray level co-occurrence matrix (GLCM), curvelet, and combined radiomics features selected by multiple algorithms
Evaluating machine learning models for post-surgery treatment response assessment in glioblastoma multiforme: a comparative study of gray level co-occurrence matrix (GLCM), curvelet, and combined radiomics features selected by multiple algorithms
Sanaz Alibabaei, Masoumeh Rahmani, Marziyeh Tahmasbi, Samira Raminfard et al.
Citations
1 citations
Journal
BMC Medical Imaging
Topics
Radiomics and Machine Learning in Medical Imaging
Field-Weighted Citation Impact (FWCI)
1.36
article
Evaluating machine learning models for post-surgery treatment response assessment in glioblastoma multiforme: a comparative study of gray level co-occurrence matrix (GLCM), curvelet, and combined radiomics features selected by multiple algorithms
Sanaz Alibabaei, Masoumeh Rahmani, Marziyeh Tahmasbi, Samira Raminfard, Mohammad Yousefipour
article en
1 citations
Abstract
No abstract available for this paper.
Publication Tag
BMC Medical ImagingVol. 25(1)
Institutions
Isfahan University of Medical Sciences (IR), Ahvaz Jundishapur University of Medical Sciences (IR), Shahid Chamran University of Ahvaz (IR), Imam Khomeini Hospital (IR), Semnan University of Medical Sciences (IR), Tehran University of Medical Sciences (IR)