Hybrid Radiomics and Machine Learning for Brain Tumors Multi‐Task Classification: An Exploratory Study on Integrating GLCM and Curvelet‐Based Features for Enhanced Accuracy

ABSTRACT Background and Aims Accurate classification of brain tumors is vital for effective treatment planning. Manual assessment of magnetic resonance imaging (MRI) scans is often subjective and time‐consuming. This exploratory study proposes a machine learning approach integrating radiomic feature...

Ausführliche Beschreibung

Bibliografische Detailangaben

Hauptverfasser:	Mostafa Jafari, Masoumeh Rahmani, Sanaz Alibabaei, Jafar Fatahiasl, Sasan Razmjoo, Mohammad Javad Tahmasebi Birgani, Marziyeh Tahmasbi
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Wiley 2025-08-01
Schriftenreihe:	Health Science Reports
Schlagworte:	brain tumor classification curvelet transform feature selection GLCM machine learning MRI radiomics
Online-Zugang:	https://doi.org/10.1002/hsr2.71195
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