A Comparison on Image, Numerical and Hybrid based Deep Learning for Computer-aided AD Diagnostics
Abstract: Alzheimer’s disease (AD) is the most common form of dementia making up 60- 70% of the 50 million active cases worldwide and is a degenerative disease which causes irreversible damage to the parts of the brain associated with the ability of thinking and memorizing. A lot of time and effort has been put towards diagnosing and detecting AD in its early stages and a field showing great promise in aiding with early stage detection is deep learning. The main issues with deep learning in the field of AD detection is the lack of relatively big datasets that are typically needed in order to train an accurate deep learning network. This paper aims to examine whether combining both image based and numerical data from MRI scans can increase the accuracy of the network. Three different deep learning neural network models were constructed with the TensorFlow framework to be used as AD classifiers using numerical, image and hybrid based input data gathered from the OASIS-3 dataset. The results of the study showed that the hybrid model had a slight increase in accuracy compared to the image and numerical based models. The report concluded that a hybrid based AD classifier shows promising results to being a more accurate and stable model but the results were not conclusive enough to give a definitive answer.
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