In this paper, we thoroughly investigate the power of Deep Convolutional Neural Networks (ConvNets) for classification of brain tumors using multi-sequence MR images. First we propose three ConvNets, 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) pre-trained on ImageNet dataset, through fine-tuning of the last few layers. Leave-one-patient-out (LOPO) testing scheme is used to evaluate the performance of the ConvNets. Results demonstrate that ConvNet achieves 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 97 % without any additional effort towards extraction and selection of features. We also study the properties of self-learned kernels/filters in different layers, through visualization of the intermediate layer outputs.

Brain tumor detection and classification from multi-sequence MRI: study using convnets

Banerjee S.;Mitra S.;Masulli F.;Rovetta S.
2019-01-01

Abstract

In this paper, we thoroughly investigate the power of Deep Convolutional Neural Networks (ConvNets) for classification of brain tumors using multi-sequence MR images. First we propose three ConvNets, 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) pre-trained on ImageNet dataset, through fine-tuning of the last few layers. Leave-one-patient-out (LOPO) testing scheme is used to evaluate the performance of the ConvNets. Results demonstrate that ConvNet achieves 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 97 % without any additional effort towards extraction and selection of features. We also study the properties of self-learned kernels/filters in different layers, through visualization of the intermediate layer outputs.
2019
978-3-030-11722-1
978-3-030-11723-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1058402
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