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Machine Learning-supported MRI Analysis of Brain Asymmetry for Early Diagnosis of Dementia

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Venue: Online

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Abstract:

Degenerative changes in the brain might lead to cognitive decline and progress further to severe dementia. The pattern of changes can be examined using structural and functional magnetic resonance imaging. The current research investigates structural changes in the degree of asymmetry between the left and right hemispheres of the human brain and analyses the levels of asymmetry using statistical features engineering. The diagnostic potential of these features and segmented MRI asymmetries are explored using variants of Support Vector Machines and a Convolutional Neural Network.

Bio:

Nitsa is a PhD student at Birkbeck College University of London since 2018. She was graduated in Medicine (MD) and Computer Science and Technologies (MSc of Biometrics). Her research interests are in computer vision, image processing, pattern recognition and machine learning. During the last five years, Nitsa focused on the development of computational algorithms for medical diagnostics. The current study is targeted to early diagnostics of Mild Cognitive Impairment using computer vision and machine learning techniques.

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Speakers
  • Nitsa Herzog