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Skin Cancer Image Classification with Pre-trained Convolutional Neural Network Architectures
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
Bildklassificering av hudcancer med förtränade konvolutionella neurala nätverksarkitekturer (Swedish)
Abstract [en]

In this study we compare the performance of different pre-trained deep convolutional neural network architectures on classification of skin lesion images. We analyse the ISIC skin cancer image dataset. Our results indicate that the architectures analyzed achieve similar performance, with each algorithm reaching a mean five-fold cross-validation ROC AUC value between 0.82 and 0.89. The VGG-11 architecture achieved highest performance, with a mean ROC AUC value of 0.89, despite the fact that it performs considerably worse than some of other architectures on the ILSVRC task. Overall, our results suggest that the choice of architecture may not be as crucial on skin-cancer classification compared with the ImageNet classification problem.

Abstract [sv]

I denna studie jämför vi hur väl olika förtränade konvolutionella neurala nätverksarkitekturer klassificerar bilder av potentiellt maligna födelsemärken. Detta med hjälp av datasetet ISIC, innehållande bilder av hudcancer. Våra resultat indikerar att alla arkitekturer som undersöktes gav likvärdiga resultat vad gäller hur väl de kan avgöra huruvida ett födelsemärke är malignt eller ej. Efter en femfaldig korsvalidering nådde de olika arkitekturerna ett ROC AUC-medelvärde mellan 0.82 och 0.89, där nätverket Vgg-11 gjorde allra bäst ifrån sig. Detta trots att samma nätvärk är avsevärt sämre på ILSVRC. Sammantaget indikterar våra resultat att valet av arkitektur kan vara mindre viktigt vid bildklassificering av hudcancer än vid klassificering av bilder på ImageNet.

Place, publisher, year, edition, pages
2019. , p. 23
Series
TRITA-EECS-EX ; 2019:344
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-259622OAI: oai:DiVA.org:kth-259622DiVA, id: diva2:1352561
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Examiners
Available from: 2019-09-24 Created: 2019-09-19 Last updated: 2019-09-24Bibliographically approved

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