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A comparison of machine learning algorithms for automatic classification of neurons by their morphology
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
En jämförelse av maskin inlärningsalgoritmer för automatiserad klassificering av neuroner utifrån deras morfologi (Swedish)
Abstract [en]

Classification of neurons has been a studied topic in neuroscience for several years and with the increase of data, new methods are encouraged to help with the classification. This study compares different machine learning algorithms to see which are better suited for classification of large data sets of morphological reconstructions in multidimensional feature space. Ten algorithms were compared on a data set of over 10 000 samples of mice neurons. Further, each classifiers ability to classify each available cell type were also investigated. The results show that Random Forest had the best overall mean accuracy followed by Multi-layer Perceptron with 83% and 78% respectively. However, observing the classification of individual cell types all the algorithms varied in accuracy and Random Forest was not considered the best. In conclusion, machine learning algorithms are a viable source when classifying neurons but more research needs to be performed to reach the higher accuracy results.

Abstract [sv]

Klassificering av nervceller har varit ett studerat ämne inom neurovetenskap i flera år och med ökningen av data uppmuntras nya metoder att hjälpa med klassificeringen. I denna studie jämförs olika maskininlärningsalgoritmer för att se vilka som passar bättre för klassificering av stora datamängder av morfologiska rekonstruktioner i multidimensionell karakteristisk rymd. Tio algoritmer jämfördes på en datamängd med över 10 000 prover av nervceller från möss. Vidare undersöktes också varje algoritms förmåga att klassificera varje enskild celltyp. Resultaten visar att Random Forest hade den bästa övergripande medelprecisionen följt av Multi-layer Perceptron med 83% respektive 78%. Fortsättningsvis observerades klassificeringen av enskilda celltyper att alla algoritmerna varierade i precision och Random Forest ansågs inte vara den bästa i varje fall. Sammanfattningsvis är maskininlärningsalgoritmer användbara verktyg för att klassificera nervceller, men mer forskning behövs göras för att nå högre precision.

Place, publisher, year, edition, pages
2018.
Series
TRITA-EECS-EX ; 2018:226
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-229749OAI: oai:DiVA.org:kth-229749DiVA, id: diva2:1214317
Subject / course
Computer Science
Supervisors
Examiners
Available from: 2018-08-03 Created: 2018-06-06 Last updated: 2018-08-03Bibliographically approved

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