Ensemble‐based glioma grade classification using Gabor filter bank and rotation forest. Issue 15 (12th February 2021)

• Record Type: Journal Article
• Authors: Singh, Rahul; Goel, Aditya; Raghuvanshi, D.K.
• Is Part Of: IET image processing. Volume 14:Issue 15(2020)
• Page Start: 3851
• Page End: 3858
• Publication Date: 2021-02-12
• Subjects: pattern classification -- feature extraction -- learning (artificial intelligence) -- biomedical MRI -- Gabor filters -- image segmentation -- support vector machines -- brain -- image classification -- Bayes methods -- medical image processing -- independent component analysis -- tumours; rotation forest -- classification purpose -- independent components analysis -- feature subset -- independent features -- k feature subsets -- different rotations -- classifier diversity -- synthetic minority over‐sampling technique -- state‐of‐the‐art ensemble‐based -- deep learning‐based -- traditional machine learning‐based classification -- Gabor filter bank -- automated ensemble‐based glioma grade classification framework -- high‐grade glioma -- discriminant features -- feature set -- base classifiers; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.0908
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : This work aims at developing an automated ensemble‐based glioma grade classification framework that classifies glioma into low‐grade glioma (LGG) and high‐grade glioma (HGG). Discriminant features are extracted using the Gabor filter bank and concatenated in a vectorised form. The feature set is then divided into k subsets of features. An ensemble of base classifiers known as rotation forest is employed for classification purpose. Independent components analysis (ICA) is applied on every feature subset and independent features are extracted. Each classifier in the ensemble is trained with these independent features from all the subset of features. These k feature subsets are responsible for different rotations during the training phase. This results in classifier diversity in the ensemble. Extensive experiments are conducted on benchmark BraTS 2017 data set and comparative analysis reveals that the proposed framework outperforms the competitive techniques in terms of various performance metrics. Data‐augmentation technique, synthetic minority over‐sampling technique is applied to oversample minority class samples alleviate class biasness problem. The proposed classification framework achieves an accuracy of 98.63%, dice similarity coefficient of 0.98 and sensitivity of 0.96. The authors conduct different comparative experiments with state‐of‐the‐art ensemble‐based, deep learning‐based and traditional machine learning‐based classification approaches to validate the performance of the proposed framework.