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An evaluation of image preprocessing for classification of Malaria parasitization using convolutional neural networks
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 utvärdering av bildförbehandlingsmetoder för klassificering av malariaparasiter med hjälp av Convolutional Neural Networks (Swedish)
Abstract [en]

In this study, the impact of multiple image preprocessing methods on Convolutional Neural Networks (CNN) was studied. Metrics such as accuracy, precision, recall and F1-score (Hossin et al. 2011) were evaluated. Specifically, this study is geared towards malaria classification using the data set made available by the U.S. National Library of Medicine (Malaria Datasets n.d.). This data set contains images of thin blood smears, where uninfected and parasitized blood cells have been segmented.

In the study, 3 CNN models were proposed for the parasitization classification task. Each model was trained on the original data set and 4 preprocessed data sets. The preprocessing methods used to create the 4 data sets were grayscale, normalization, histogram equalization and contrast limited adaptive histogram equalization (CLAHE).

The impact of CLAHE preprocessing yielded a 1.46% (model 1) and 0.61% (model 2) improvement over the original data set, in terms of F1-score. One model (model 3) provided inconclusive results. The results show that CNN’s can be used for parasitization classification, but the impact of preprocessing is limited.

Abstract [sv]

I denna studie studerades effekten av flera bildförbehandlingsmetoder på Convolutional Neural Networks (CNN). Mätvärden såsom accuracy, precision, recall och F1-score (Hossin et al. 2011) utvärderades. Specifikt är denna studie inriktad på malariaklassificering med hjälp av ett dataset som tillhandahålls av U.S. National Library of Medicine (Malaria Datasets n.d.). Detta dataset innehåller bilder av tunna blodutstryk, med segmenterade oinfekterade och parasiterade blodceller.

I denna studie föreslogs 3 CNN-modeller för parasiteringsklassificeringen. Varje modell tränades på det ursprungliga datasetet och 4 förbehandlade dataset. De förbehandlingsmetoder som användes för att skapa de 4 dataseten var gråskala, normalisering, histogramutjämning och kontrastbegränsad adaptiv histogramutjämning (CLAHE).

Effekten av CLAHE-förbehandlingen gav en förbättring av 1.46% (modell 1) och 0.61% (modell 2) jämfört med det ursprungliga datasetet, vad gäller F1-score. En modell (modell 3) gav inget resultat. Resultaten visar att CNN:er kan användas för parasiteringsklassificering, men effekten av förbehandling är begränsad.

Place, publisher, year, edition, pages
2019. , p. 29
Series
TRITA-EECS-EX ; 2019:370
Keywords [en]
Deep Learning, Convolutional Neural Network, Malaria, Image Recognition, Preprocessing, Computer Aided Diagnosis, Grayscale, Normalization, Histogram Equalization, CLAHE.
Keywords [sv]
Deep Learning, Convolutional Neural Network, Malaria, Image Recognition, Preprocessing, Computer Aided Diagnosis, Grayscale, Normalization, Histogram Equalization, CLAHE.
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-260090OAI: oai:DiVA.org:kth-260090DiVA, id: diva2:1354567
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Examiners
Available from: 2019-10-09 Created: 2019-09-25 Last updated: 2019-10-09Bibliographically approved

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