Detection of early stages of Parkinson's disease by analyzing fMRI data and machine learning approaches

Detection of early stages of Parkinson's disease by analyzing fMRI data and machine learning approaches

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.

Bibliografiske detaljer
Main Authors:	Neehal, Ahmed Hasin, Azam, Md. Nura, Islam, Md. Sazzadul, Hossain, Md. Ishrak
Andre forfattere:	Parvez, Mohammad Zavid
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2020
Fag:	Functional imaging Parkinson's disease fMRI Voxel intensity Machine learning SVM classifier STFT
Online adgang:	http://hdl.handle.net/10361/13884

Lignende værker

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Impact of Lifestyle on the Disease Progression of Parkinson’s
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fMRI
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Visual object classification from fMRI data
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Detection of Parkinson’s disease from Neuro-imagery using deep neural network with transfer learning
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Parkinson’s disease and COVID-19: treatment and patient management during a pandemic
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In-depth analysis of deep learning architectures for brain tumor classification in MRI scans
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Stress detection for visually impaired people using EEG signals based on extracted features from time-frequency domain
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An Efficient deep learning approach to detect Brain Tumor using MRI images
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Induced pluripotent stem cells: a novel tool for modelling and treating Parkinson’s disease
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Gender classification in Bangla language using deep learning-based voice analysis
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Multi-classification based Alzheimer's disease detection with comparative analysis from brain MRI scans using deep learning
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Pending Issue of design coordination for 1F-3F construction
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Analyzing data of social media to evaluate customer behavior of companies using sentiment analysis
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