Titlebook: Data Analysis for Neurodegenerative Disorders; Deepika Koundal,Deepak Kumar Jain,Atef Zaguia Book 2023 The Editor(s) (if applicable) and T

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Electroencephalogram Analysis Using Convolutional Neural Networks in Order to Diagnose Alzheimer’s Dnt technical advances have enabled the collection of biomarkers in areas where early symptoms co-occur. This goal, which is important for finding Alzheimer‘s disease and its symptoms quickly and accurately, could be achieved in a way that is helpful. It is critical to attain this goal to have a way

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Machine Learning Models for Alzheimer’s Disease Detection Using Medical Imagestrophy resulting in multiple neurodegenerative diseases. Neuro-imaging is the most critical method for the detection Alzheimer’s and quantification of brain atrophy. Magnetic resonance imaging (MRI), computed tomography (CT), single-photo emission computes tomography (SPECT), and positron emission t

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Machine Learning Models for Diagnosing Alzheimer’s Disorderspromising tool in early diagnosis and prediction of Alzheimer. One popular approach is to use neuroimaging data to train ML models to classify individuals as either having Alzheimer’s disease or being healthy. These models can also be used to predict the progression of the disease in individuals ove

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Alzheimer’s Disease Diagnosis Using MRI Images popular belief. Some of the claims stated here are not supported by sources. Alzheimer’s disease and other kinds of neurodegeneration frequently manifest their first signs as dementia. People with poor intellect may struggle to discern between a sick and a healthy reference group. Our system can ef

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The Colossal Impact of Machine Learning Models on Parkinson’s Disorder: A Comparative Analysis55?years old. Populations in Japan, Germany, Italy, Hong Kong, and Greece have median ages exceeding 45. Parkinson‘s disease is one of several illnesses that are increasingly prevalent as people get older. Parkinson‘s disease is one of the most common neurological conditions, impacting the majority

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