2018
DOI: 10.48550/arxiv.1806.07589
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A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks

Abstract: Inspired by the success of Convolutional Neural Networks (CNN), we develop a novel Computer Aided Detection (CADe) system using CNN for Glioblastoma Multiforme (GBM) detection and segmentation from multi channel MRI data. A two-stage approach first identifies the presence of GBM. This is followed by a GBM localization in each "abnormal" MR slice. As part of the CADe system, two CNN architectures viz. Classification CNN (C-CNN) and Detection CNN (D-CNN) are employed. The CADe system considers MRI data consistin… Show more

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Cited by 3 publications
(3 citation statements)
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“…with a random initialization of the parameters) is used as baseline. The baseline in structured as in [45], because of the similarities between our approach and the one designed by authors for detecting brain tumors. The input shape of the network is 224 × 224 × 3, thus compliant with the dataset described in Section II.…”
Section: A Network Architectures and Transfer Learningmentioning
confidence: 99%
“…with a random initialization of the parameters) is used as baseline. The baseline in structured as in [45], because of the similarities between our approach and the one designed by authors for detecting brain tumors. The input shape of the network is 224 × 224 × 3, thus compliant with the dataset described in Section II.…”
Section: A Network Architectures and Transfer Learningmentioning
confidence: 99%
“…The study of Xu et al 47 aimed to reduce FPs in the segmentation task rather than object detection. Banerjee et al 48 suggested an image processing-based two-stage method reduce FP cases in their study that segmented with brain MRI. The study of Naceur et al 49 is again a study that reveals the FP problem.…”
Section: Related Workmentioning
confidence: 99%
“…Nevertheless, it is a fact that these models struggle with performance issues when they are not sufficiently balanced. So, a compelling need for more robust methods to enhance their performance is required [8]. Existing methods to improve the performance of CNN models in the medical domain include domain adaptation and transfer learning [9].…”
Section: Introductionmentioning
confidence: 99%