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Evaluation of a Radiomics Model for Classification of Lung Nodules
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Utvärdering av en Radiomics-modell för klassificering av lungnoduler (Swedish)
Abstract [en]

Lung cancer has been a major cause of death among types of cancers in the world. In the early stages, lung nodules can be detected by the aid of imaging modalities such as Computed Tomography (CT). In this stage, radiologists look for irregular rounded-shaped nodules in the lung which are normally less than 3 centimeters in diameter. Recent advancements in image analysis have proven that images contain more information than regular parameters such as intensity, histogram and morphological details. Therefore, in this project we have focused on extracting quantitative, hand-crafted features from nearly 1400 lung CT images to train a variety of classifiers based on them. In the first experiment, in total 424 Radiomics features per image has been used to train classifiers such as: Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Linear Discriminant Analysis (LDA) and Multi-Layer Perceptron (MLP). In the second experiment, we evaluate each feature category separately with our classifiers. The third experiment includes wrapper feature selection methods (Forward/Backward/Recursive) and filter-based feature selection methods (Fisher score, Gini Index and Mutual information). They have been implemented to find the most relevant feature set in model construction. Performance of each learning method has been evaluated by accuracy score, wherewe achieved the highest accuracy of 78% with Random Forest classifier (74% in 5-fold average) and 0.82 Area Under the Receiver Operating Characteristics (AUROC) curve. After RF, NB and MLP showed the best average accuracy of 71.4% and 71% respectively.

Place, publisher, year, edition, pages
2019. , p. 50
Series
TRITA-CBH-GRU ; 2019:109
Keywords [en]
Lung Nodule, Radiomics, Tumor Classification
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-261623OAI: oai:DiVA.org:kth-261623DiVA, id: diva2:1359286
Subject / course
Medical Engineering
Educational program
Master of Science - Medical Engineering
Presentation
2019-09-10, T2, Hälsovägen 11C,141 57 HUDDINGE, Stockholm, 13:00 (English)
Supervisors
Examiners
Available from: 2019-10-11 Created: 2019-10-08 Last updated: 2019-10-11Bibliographically approved

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