Dr Noor Kamal Al-Qazzaz: is currently a Lecturer at the Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Iraq. She received her BSc in Biomedical Engineering from Baghdad University in 2003 and MSc in Medical Engineering from Nahrain University in 2006, Iraq. Dr. Noor received a Ph.D. in 2016 from the Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Malaysia. Her research interests are biomedical engineering, Biosignal processing, feature extraction, machine learning, Deep learning, vascular dementia, Brain computer interface (BCI), Emotion, Electroencephalogram (EEG) memory and neuropsychological assessment. Dr Noor's received the best paper award of the second International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) in 2018. She was one of the final 12 BCIAward2021, global nominees, for her unique machine learning methodologies in a motor imagery-based BCI system and considerable accuracy increase.
PhD
Associate Professor
Scientific committee member Promotion committee member
Biomedical signal processing, cognitive science, dementia, machine learning, deep learning, memory, brain-computer interface, emotion recognition, and autism detection
Biomedical Engineering
medical image processing digital signal processing Biostatistics Advances Biomedical Image processing Techniques Neural Interfaces
Under-graduate students MSc students
Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated
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