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A Comparative Study of the Effect of Features on Neural Networks within Computer-Aided Diagnosis of Alzheimer's Disease
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
En jämförelsestudie av oberoende variablers inverkan på neuronnät inom datorstödd diagnos av Alzheimers sjukdom (Swedish)
Abstract [en]

Alzheimer’s disease is a neurodegenerative disease that affects approximately 6% of the global population aged over 65 and is forecasted to become even more prevalent in the future. Accurately diagnosing the disease in an early stage can play a large role in improving the quality of life for the patient. One key development for performing this diagnosis is applying machine learning to perform computer-aided diagnosis. Current research in the field has been focused on removing assumptions about the used data sets, but in doing so they have often discarded objective metadata such as the patient’s age, sex or priormedical history. This study aimed to investigate the effect of including such metadata as additional input features to neural networks used for diagnosing Alzheimer’s disease through binary classification of magnetic resonance imaging scans. Two similar neural networks were developed and compared, one with these additional features and the other without them. Including the metadata led to significant improvements in the network’s classification accuracy, and should therefore be considered in future computer-aided diagnostic systems for Alzheimer’s disease.

Abstract [sv]

Alzheimers sjukdom är en form av demens som påverkar ungefär 6% av den globala befolkningen som är äldre än 65 och förutspås bli ännu vanligare i framtiden. Tidig diagnos av sjukdomen är viktigt för att säkerställa högre livskvalitet för patienten. En viktig utveckling inom fältet är datorstödd diagnos av sjukdomen med hjälp av maskininlärning. Dagens forskning fokuserar på att ta bort subjektiva antaganden om datamängden som används, men har ofta även förkastat objektiv metadata såsom patientens ålder, kön eller tidigare medicinska historia. Denna studier ämnade därför undersöka om inkluderandet av denna metadata ledde till bättre prestanda hos neuronnät som används för datorstödd diagnos av Alzheimers genom binär klassificering av bilder tagna med magnetisk resonanstomografi. Två snarlika neuronnät utvecklades och jämfördes, med skillnaden att den ena även tog metadata om patienten som indata. Inkluderandet av metadatan ledde till en markant ökning i neuronnätets prestanda, och bör därför övervägas i framtida system för datorstödd diagnos av Alzheimers sjukdom.

Place, publisher, year, edition, pages
2019.
Series
TRITA-EECS-EX ; 2019:335
Keywords [en]
Alzheimer’s disease, computer-aided diagnosis, machine learning, artificial neural networks
Keywords [sv]
Alzheimers sjukdom, datorstödd diagnos, maskininlärning, artificiella neuronnät
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-255260OAI: oai:DiVA.org:kth-255260DiVA, id: diva2:1338838
Subject / course
Computer and Systems Sciences
Supervisors
Examiners
Available from: 2019-07-29 Created: 2019-07-24 Last updated: 2019-07-29Bibliographically approved

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