TensorFlow London: Deep learning for classification of Attention Deficit Hyperactive Disorder
Speaker: Atif Riaz, Researcher at City, University of London
Title: Deep learning for classification of Attention Deficit Hyperactive Disorder (ADHD) using functional MRI
Abstract: Brain disorders have emerged as one of the greatest threats to human health. Mental, neurological and substance use disorders constitute 13% of the global burden of disease exceeding both cancer and cardiovascular diseases. Despite the advances in imaging technologies, proper clinical diagnosis is not well established and in most cases diagnosis of a neurological disorder is achieved based on physical observations. Deep learning is the best available tool to help medical experts for the diagnosis of a brain disorder. The talk aims to explain the application of deep learning methods particularly CNNs, using the Tensorflow, in the medical domain for diagnosis of brain disorder and understand brain functions.
Bio: Atif is a researcher at the City, University of London. During his research, he has explored different machine learning and deep learning methods in the domain of medical imaging for the classification of brain disorders based on functional MRI data. His proposed novel methods have achieved better classification performance as compared to the state-of-the-art on the ADHD dataset. During his research, he has multiple journal and conference publications.