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题目： Advanced Machine learning techniques for Medical Applications
报告人： Abir Hussain
报告人简介：Abir Hussain is a Reader in Image and Signal Processing at the Department of Computer Science at Liverpool John Moores University, joining in 2001 as a Senior Lecturer and promoted to a Reader in 2014. Dr. Hussain graduated from Salford University with a first class honours degree in Electronic Computer systems in 1995. She then pursued MSc degree at The University of Manchester (UMIST) in control and information technology. Her master dissertation was with the paper science department, looking at the crack simulation in cellulosic fibres using fractal simulations. She completed her PhD study at The University of Manchester (UMIST) in 2000. She has experience as external research examiner for PhD and MPhil degrees, and extensive research in Image and Signal processing as well as medical support systems, neural networks, data science and data analysis and processing.
内容提要：Dr. Hussain will discuss novel neural network architecture which is based on the immune algorithm and is used for the classification of term and preterm data for pregnant women. One of the most challenging tasks currently facing the healthcare community is the identification of premature labour. In medical terms, a premature birth is when a baby arrives before 37 completed weeks of pregnancy. Figures from the UK Department of Health show that six to seven per cent of babies in the UK are born prematurely each year. The developing baby goes through important growth during the final weeks and months of pregnancy. Many organ systems, including the brain, lung, and liver, need the final weeks of pregnancy to fully develop. Predicting preterm birth and diagnosing preterm labour clearly have important consequences, for both health and the economy.