Open AccessLearning Texture Features from GLCM for Classification of Brain Tumor MRI Images using Random Forest Classifier
Author(s) -
Ashwani Kumar Aggarwal
Publication year - 2022
Publication title -
wseas transactions on signal processing
Language(s) - English
Resource type - Journals
eISSN - 2224-3488
pISSN - 1790-5052
DOI - 10.37394/232014.2022.18.8
Subject(s) - artificial intelligence , random forest , pattern recognition (psychology) , extractor , feature extraction , computer science , classifier (uml) , confusion matrix , grey level , feature (linguistics) , confusion , contextual image classification , image (mathematics) , engineering , psychology , linguistics , philosophy , process engineering , psychoanalysis
In computer vision, image feature extraction methods are used to extract features so that the features are learnt for classification tasks. In biomedical images, the choice of a particular feature extractor from a diverse range of feature extractors is not only subjective but also it is time consuming to choose the optimum parameters for a particular feature extraction algorithm. In this paper, the focus is on the Grey-level co-occurrence matrix (GLCM) feature extractor for classification of brain tumor MRI images using random forest classifier. A dataset of brain MRI images (245 images) consisting of two classes viz. images with tumor (154 images) and images without tumor (91 images) has been used to assess the performance of GLCM features on random forest classifier in terms of accuracy, true positive rate, true negative rate, false positive rate, false negative rate derived from the confusion matrix. The results show that by using optimum parameters, the GLCM feature extracts significant texture component in brain MRI images for promising accuracy and other performance metrics.