Texture Feature Analysis Of Mri-adc Images To Differentiate Glioma Grades Using Machine Learning Techniques

This study focuses on developing a robust machine learning model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients’ demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI). A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected and images were acquired using 3T-MR systems. Skewness, kurtosis, and statistical texture features of GLCM were calculated using ADC values within the region of interest (ROI). The ANOVA f-test was utilized to select the best features to train an ML model and the random forest classifier was selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14%.