Alzheimer's disease diagnostics by a deeply supervised adaptable 3D convolutional network

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dc.contributor.authorMohamad, Ghazal
dc.contributor.authorGimel farb, Georgy
dc.contributor.authorAsl, Ehsan Hosseini
dc.date.accessioned2019-03-12T06:39:09Z
dc.date.accessioned2023-08-19T09:10:11Z
dc.date.available2019-03-12T06:39:09Z
dc.date.available2023-08-19T09:10:11Z
dc.date.issued2016
dc.descriptionHosseini-Asl, E., Gimel'farb, G., & El-Baz, A. (2016). Alzheimer's disease diagnostics by a deeply supervised adaptable 3D convolutional network. arXiv preprint arXiv:1607.00556.en_US
dc.description.abstractEarly diagnosis, playing an important role in preventing progress and treating the Alzheimer's disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, eg, ventricles size, hippocampus shape, cortical thickness, and brain volume.en_US
dc.identifier.citationhttps://arxiv.org/pdf/1607.00556.pdfen_US
dc.identifier.urihttps://edms.wexl.in/handle/1/1697
dc.language.isoen_USen_US
dc.publisherCornell universityen_US
dc.subjectAlzheimer’s diseaseen_US
dc.subjectDeep learningen_US
dc.subject3D convolutional neural networken_US
dc.subjectAutoencoderen_US
dc.subjectBrain MRIen_US
dc.titleAlzheimer's disease diagnostics by a deeply supervised adaptable 3D convolutional networken_US
dc.typeArticleen_US

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