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Machine learning application in Glioma classification: review and comparison analysis

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Abstract

This paper simply presents a state of the art survey among the machine learning based approaches for the Glioma classification. As Glioma classification is a very challenging task in the field of medical science and this task is well addressed and taken by the fraternity of machine learning experts, who are working day and night to devise automated approaches that can automate this whole process of Glioma accurate classification from the various medical imaging modalities like Magnetic resonance imaging (MRI), Computed tomography (CT) etc. Although present machine learning techniques offers an opportunity to come up with a highly accurate and automated Glioma classification approach, by performing fusion among the various medical imaging modalities as well as utilizing the various features derived from the multi-modality medical imaging data. This paper also proposed an efficient and accurate automated approach of Glioma classification for the comparison analysis. This proposed approach is based on the use of hybrid ensemble learning model and hybrid feature extraction method, which relies on the Discrete wavelet Decomposition (DWD), Central pixel Neighbourhood Binary pattern (CNBP) and GLRLM (Gray level run length Matrix) methods in order to classify the Glioma (type of mostly diagnosed brain tumors) into Low grade Glioma and High grade Glioma from the fused MRI sequences. Improved eXtreme Gradient Boosting classifier is the hybrid ensemble learning model, which is used in this paper for the first time along with the proposed hybrid texture feature extraction method. Further this proposed approach is compared with the already existing state of the art approaches, which are based on the various machine learning classifiers like Support vector machine (SVM), K-Nearest neighbor (KNN), Naïve Bayes (NB) etc. and conventional feature extraction methods in order to present a comprehensive and practical comparison study. The proposed approach is evaluated on the balanced large size local dataset consisting of MRI images of low and high grade Glioma collected from the various MRI centers located in Madhya Pradesh, India as well as on the popular global datasets like, BRATS 2013 and BRATS 2015 with various MRI fusion combinations (T1 + T1C + T2 + Flair, T1 + T1C + T2, T1 + T1C + Flair, T1C + T2 + Flair etc.). The proposed approach employing Improved eXtreme Gradient Boosting ensemble model offers highest accuracy of above 90% on the local dataset with the fusion of T1C + T2 + Flair MRI sequences.

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Acknowledgements

We like to thank the MP MRI and CT scan centre at Netaji Subhash Chandra Bose Medical College, Jabalpur, Sanya Diagnostic MRI center, Bhopal and Cancer Hospital Gwalior for providing the clinical MRI dataset of Brain MRI images. I am thankful to all the radiologists for providing their valuable support in terms of providing data, Knowledge and validate this proposed work.

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Authors and Affiliations

  1. Rajiv Gandhi Technical University, Bhopal, India

    Kirti Raj Bhatele

  2. MITS, Gwalior, India

    Sarita Singh Bhadauria

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  1. Kirti Raj Bhatele
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  2. Sarita Singh Bhadauria
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Correspondence to Kirti Raj Bhatele.

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The authors have declared no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors. The Local dataset consist of only MRI images of low and high grade glioma brain tumour collected from the MP MRI and CT scan centre at Netaji Subhash Chandra Bose Medical College, Jabalpur, Sanya Diagnostic MRI center, Bhopal and Cancer Hospital Gwalior, India and is in accordance with the ethical standards of these institutions. The other two datasets i.e. BRATS 2013 and 2015 are publicly available datasets.

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Bhatele, K.R., Bhadauria, S.S. Machine learning application in Glioma classification: review and comparison analysis. Arch Computat Methods Eng 29, 247–274 (2022). https://doi.org/10.1007/s11831-021-09572-z

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