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A Deep Learning Approach For Identifying Key Biomarkers In Medical Imaging Applications, David Odaibo
A Deep Learning Approach For Identifying Key Biomarkers In Medical Imaging Applications, David Odaibo
All ETDs from UAB
Although Deep Leaning has achieved great success in many domains in recent years, research into its applicability and effectiveness in medical applications has been limited for various reasons. Some of these barriers are related to the historic impression that neural networks are black boxes, especially when applied to medical diagnosis and offer no in terpretation into prediction. In this dissertation, I demonstrated that deep learning can be an effective utility in medical diagnosis and biomarker detection. In particular, I have developed deep learning models that can identify biomarkers in brain hemorrhage, brain tumor, pneumonia, diabetic retinopathy and Parkinsons disease, with …
A Deep Learning Framework For Medical Image Segmentation, Zheng Zhang
A Deep Learning Framework For Medical Image Segmentation, Zheng Zhang
All ETDs from UAB
Deep Learning (DL) has rapidly become a methodology of choice for analyzing medical images and increasingly attracts researcher’s attention in the medical research community. The brain is one of the most important organs in the human body. Within the context of human brain disease, research and care, accurately detecting, evaluating and segmenting human brain abnormalities play an important role in brain disease diagnosis, prognosis, and treatment planning. A significant challenge in developing good brain abnormalities segmentation methods is the high variation of brain abnormalities such as differences in shape, size, location, appearance, and regularity. Deep Learning approach ad-dresses this challenge …
The Real-Time Extractiion Of Neural Spikes For Brain-Machine Interface Application Using Deep Learning Algorithm, Sahaj Anilbhai Patel
The Real-Time Extractiion Of Neural Spikes For Brain-Machine Interface Application Using Deep Learning Algorithm, Sahaj Anilbhai Patel
All ETDs from UAB
The detection of neural spikes in real-time and accurately has become the center of interest for the researchers in the field of brain-machine/computer interface (BMI/BCI). The primary challenge in the Brain-Machine interface is to translate raw neuronal response signals into the control of electrical actuators. Only accurate and rapid classification of neural response can help efficiently and conveniently to disable peoples, particularly those suffering from spinal cord injury, stocks, etc., Recording from neurons and analyzing them with many different methods are not new. However, the primary challenge here is the real-time recording and classifying the spikes with higher accuracy. In …