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Original Article 

Machine Learning For Alzheimer's Disease Diagnosis: Computer Vision and Recurrent Neural Networking

Jack Diskin

J. Dawn. Research, Vol. 4, pp 3-24, 2022

In 2013, the Virginia Medical Center found that 12 million Americans are impacted by clinical diagnostic errors each year — mistakes that result in 40,000 to 80,000 annual fatalities. The purpose of this study was to address this issue through the implementation of convolutional neural network (CNN) and long-short term memory (LSTM) machine learning algorithms in python, trained for dementia classification via MRI and demographic data. The CNN is a computer vision model capable of 2-D feature extraction through kernel convolution and pooling operations, in tandem with interconnected vectors (dense layers), containing individual elements referred to as neurons, which provide class predictions. The LSTM is a recurrent neural network composed of independent computational cells designed to perform sequential data analysis. Each of these cells are defined by a cell state, to which information can be appended or removed via regulatory gates that utilize the sigmoid function and pointwise multiplication to make decisions. […]


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