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A brain stroke detection method based on deep learning method

Xiuzhu Ye  
【摘要】:In this paper,two deep-learning based microwave imaging algorithms are proposed to detect hemorrhagic stroke in a non-invasive way.Since the electromagnetic properties of a human brain are complicated,the inversion problem of the brain is of high nonlinearity.The direct microwave imaging of the human brain is easily plagued by degeneracy issues and with modest imaging quality.However,the structural differences between human brains are minimal and the relative permittivity of a normal brain can be known in advance.By introducing such prior information,the normal brain can be treated as the inhomogeneous background,thus reducing the non-linearity of the problem and regularizing the search space.In this way,a backpropagation scheme based on convolutional neural network(CNN) under the inhomogeneous background setting is designed.Two methodologies are then taken to locate the hemorrhagic point of stroke.First method is that CNN yields directly the possibilities of being the hemorrhagic point within the brain area.Second,CNN retrieves the relative permittivity with inhomogeneous background.The CNN are designed and trained differently according to their distinguishing functionalities.A bunch of 2 D numerical models of normal and stroke brain with lossy permittivity are established to train the neural network and show the performance of these two algorithms.It turns out that the treatment of normal brain as the inhomogeneous background leverages the prior knowledge of human brain,reduce the calculation intensity,rectify the search space and therefore improve the imaging quality.In addition,by adopting the deep-learning scheme,the algorithms are able to locate the hemorrhagic point of stroke swiftly and accurately.Our work serves as a promising pathway towards a microwave stroke detection system.

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