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Diagnostic Categorization and Neurocognitive Prediction Employing Neuroimaging Data Using Deep Learning in Alzheimer's Illness
Abstract
Traditional analytic strategies for investigating neuroimaging biomarkers for neuropsychiatric illnesses have relied on mass univariate statistics, assuming that various brain areas function separately. Machine learning (ML) methods that take into account intercorrelation across areas have recently become a popular and important part of computer-assisted analytical procedures and are now frequently used for the automated diagnosis and analysis of neuropsychiatric illnesses. The goal of this chapter is to provide a detailed overview of CNN and RNN applications in medical image comprehension. The overarching goal is to encourage medical image understanding experts to use CNNs extensively in their research and diagnosis. This chapter describes the development of various novel DL-based approaches and models as well as advancements in high-speed computing techniques, which provide a once-in-a-lifetime chance to anticipate and control Alzheimer's disease.
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