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Abstract #4739

Deep convolutional neural networks for brain lesion segmentation in multiple sclerosis using clinical MRI scans

Sunny Nagam1, Glen Pridham1, and Yunyan Zhang1

1University of Calgary, Calgary, AB, Canada

Machine learning opens up a new opportunity for advancing our image pattern recognition abilities in medical imaging. In this study, we tested the potential of 3 new deep convolutional neural network-based learning methods for detecting brain MRI lesions in multiple sclerosis (MS). Using clinical scans available online from 10 patients, we found that the ResNet and SegNet achieved a promising dice score of 0.65 and 0.61 respectively, better than the generative adversarial network. Deep learning methods may be novel tools for optimal detection of brain MRI lesions, improving the management of patients with MS and similar disorders.

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