NDLI: Ensemble-based glioma grade classification using Gabor filter bank and rotation forest

Content Provider	IET Digital Library
Author	Singh, Rahul Goel, Aditya Raghuvanshi, D. K.
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.
Starting Page	3851
Ending Page	3858
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 15, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0908
Journal	IET Image Processing
Publisher Date	2020-12-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automated Ensemble-based Glioma Grade Classification Framework Base Classifier Bayes Method Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Brain Classification Purpose Classifier Diversity Combinatorial Mathematics Computer Vision And Image Processing Technique Data Handling Technique Deep Learning-based Different Rotations Discriminant Features Feature Extraction Feature Set Feature Subset Gabor Filter Gabor Filter Bank High-grade Glioma Image Classification Image Recognition Image Segmentation Independent Component Analysis Independent Feature K Feature Subsets Knowledge Engineering Technique Learning in AI Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Pattern Classification Rotation Forest Spectroscopy State-of-the-art Ensemble-based Statistics Support Vector Machine Synthetic Minority Over-sampling Technique Traditional Machine Learning-based Classification Tumours
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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