Classification of Alzheimer’s Disease From MRI Images
| dc.contributor.advisor | Avots, Egils, supervisor | |
| dc.contributor.advisor | Anbarjafari, Gholamreza, supersvisor | |
| dc.contributor.author | Elshatoury, Heba Hesham Hamed | |
| dc.date.accessioned | 2019-06-04T08:57:10Z | |
| dc.date.available | 2019-06-04T08:57:10Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | In English: In this thesis work machine learning techniques are used to classify MRI brain scans of people with Alzheimers Disease. This work deals with binary classification between Alzheimers Disease (AD) and Cognitively Normal (CN). Supervised learning algorithms were used to train a classifier using MATLAB Classification Learner App in which the accuracy is being compared. The dataset used is from The Alzheimers Disease Neuroimaging Initiative (ADNI). Histogram is used for all slices of all images. Based on the highest performance, specific slices were selected for further examination. Majority voting and weighted voting is applied in which the accuracy is calculated and the best result is 69.5% for majority voting. Eesti keeles: Käesolevas töös kasutatakse masinõppe meetodeid, et klassifitseerida Alzheimeri tõvega inimeste MRI aju skaneeringuid. Töös rakendatakse binaarset liigitust Alzheimeri tõve (AD) ja kognitiivse normaalsuse (CD) vahel. Kasutati juhendatud masinõppealgoritme, et treenida klassifikaatoreid MATLAB’i klassifikaatorite õpperakenduses (Classification Learner App), kus võrreldi algoritmi täpsust. Kasutatav andmestik pärineb ADNI andmebaasist (The Alzheimer’s Disease Neuroimaging Initiative). Kõikidest piltidest võetud osadele arvutati histogrammid. Kõrgeima jõudluse põhjal valiti konkreetsed osad edasiseks uurimiseks. Võtteldi enamus ja kaalutud valikute täpsust ja parimaks tulemuseks saadi enamusvalikuid kasutades 69.5%. | en |
| dc.identifier.uri | http://hdl.handle.net/10062/63936 | |
| dc.language.iso | eng | et |
| dc.publisher | Tartu Ülikool | et |
| dc.rights | openAccess | et |
| dc.rights | Autorile viitamine + Mitteäriline eesmärk + Tuletatud teoste keeld 3.0 Eesti | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ee/ | * |
| dc.subject | Computer Vision | en |
| dc.subject | Machine Learning | en |
| dc.subject | Alzheimer’s Disease | en |
| dc.subject | feature extraction | en |
| dc.subject | magnetic resonance imaging | en |
| dc.subject | masinõpe | et |
| dc.subject | Alzheimeri tõbi | et |
| dc.subject | magnetresonantstomograafia | et |
| dc.subject | arvutinägemine | et |
| dc.title | Classification of Alzheimer’s Disease From MRI Images | en |
| dc.title.alternative | Alzheimeri tõve klassifitseerimine MRI-piltidest | et |
| dc.type | Thesis | et |