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Glioma Classification Using Deep Radiomics

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Abstract

Glioma constitutes \(80\%\) of malignant primary brain tumors in adults, and is usually classified as high-grade glioma (HGG) and low-grade glioma (LGG). The LGG tumors are less aggressive, with slower growth rate as compared to HGG, and are responsive to therapy. Tumor biopsy being challenging for brain tumor patients, noninvasive imaging techniques like magnetic resonance imaging (MRI) have been extensively employed in diagnosing brain tumors. Therefore, development of automated systems for the detection and prediction of the grade of tumors based on MRI data becomes necessary for assisting doctors in the framework of augmented intelligence. In this paper, we thoroughly investigate the power of deep convolutional neural networks (ConvNets) for classification of brain tumors using multi-sequence MR images. We propose novel ConvNet models, which are trained from scratch, on MRI patches, slices, and multi-planar volumetric slices. The suitability of transfer learning for the task is next studied by applying two existing ConvNets models (VGGNet and ResNet) trained on ImageNet dataset, through fine-tuning of the last few layers. Leave-one-patient-out testing, and testing on the holdout dataset are used to evaluate the performance of the ConvNets. The results demonstrate that the proposed ConvNets achieve better accuracy in all cases where the model is trained on the multi-planar volumetric dataset. Unlike conventional models, it obtains a testing accuracy of \(95\%\) for the low/high grade glioma classification problem. A score of \(97\%\) is generated for classification of LGG with/without 1p/19q codeletion, without any additional effort toward extraction and selection of features. We study the properties of self-learned kernels/ filters in different layers, through visualization of the intermediate layer outputs. We also compare the results with that of state-of-the-art methods, demonstrating a maximum improvement of \(7\%\) on the grading performance of ConvNets and \(9\%\) on the prediction of 1p/19q codeletion status.

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Acknowledgements

This research is supported by the IEEE Computational Intelligence Society Graduate Student Research Grant 2017. Banerjee acknowledges the support provided to him by the Intel Corporation, through the Intel AI Student Ambassador Program. Mitra acknowledges the support provided to her by the Indian National Academy of Engineering, through the INAE Chair Professorship. This publication is an outcome of the R&D work undertaken in a project with the Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation.

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Authors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, 700108, India

    Subhashis Banerjee & Sushmita Mitra

  2. Department of Informatics Bioengineering Robotics and Systems Engineering, University of Genova, Via Dodecaneso 35, 16146, Genoa, Italy

    Francesco Masulli & Stefano Rovetta

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  1. Subhashis Banerjee
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This article is part of the topical collection “Computational Biology and Biomedical Informatics” guest edited by Dhruba Kr Bhattacharyya, Sushmita Mitra and Jugal Kr Kalita.

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Banerjee, S., Mitra, S., Masulli, F. et al. Glioma Classification Using Deep Radiomics. SN COMPUT. SCI. 1, 209 (2020). https://doi.org/10.1007/s42979-020-00214-y

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