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Comparison Of Features Used In Automatic Skin Lesion Classification

Feringa, S. (2015) Comparison Of Features Used In Automatic Skin Lesion Classification. Master's Thesis / Essay, Computing Science.

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Abstract

Malignant skin lesions are an ever more common health problem in modern society. Certain types will even result in almost certain death when left untreated. The medical science community is therefore searching for better methods for diagnosing these lesions. Computing science can help doctors with this classification problem. This work attempts to reveal how we can empower the designer of skin classification tools to effectively and efficiently explore the design space of skin lesion classification algorithms such as kNN and SVM, by focusing on classifying birthmarks and melanoma. After images have been segmented into healthy skin and skin lesion sections, a substantial group of descriptors are extracted from every segmented image. These include among others: common colour based features, statistical moments, LBP, HOG, border features and co-occurrence matrix based descriptors. Feature vectors can be explored using the application Featured. It incorporates dimensionality reduction methods to generate 2D plots of the feature space. With the help of these plots we can explore the design space of descriptors and determine the influence of specific features, which in turn help us select high quality descriptors subsets for use in classifiers. The highest classification accuracy score we achieved with our automatic classification system is 0.822, which is comparable to accuracy results attained by dermatologists. There are however still many aspects that influence the results negatively and therefore prevent the use of automatic classification systems in active medical service as an assistance tool.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Computing Science
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 08:03
Last Modified: 15 Feb 2018 08:03
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/12674

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