Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Evaluation of a Radiomics Model for Classification of Lung Nodules
KTH, Skolan för kemi, bioteknologi och hälsa (CBH).
2019 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAlternativ tittel
Utvärdering av en Radiomics-modell för klassificering av lungnoduler (svensk)
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.

sted, utgiver, år, opplag, sider
2019. , s. 50
Serie
TRITA-CBH-GRU ; 2019:109
Emneord [en]
Lung Nodule, Radiomics, Tumor Classification
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-261623OAI: oai:DiVA.org:kth-261623DiVA, id: diva2:1359286
Fag / kurs
Medical Engineering
Utdanningsprogram
Master of Science - Medical Engineering
Presentation
2019-09-10, T2, Hälsovägen 11C,141 57 HUDDINGE, Stockholm, 13:00 (engelsk)
Veileder
Examiner
Tilgjengelig fra: 2019-10-11 Laget: 2019-10-08 Sist oppdatert: 2019-10-11bibliografisk kontrollert

Open Access i DiVA

PARASTU_RAHGOZAR_STUDENT_THESIS(1239 kB)19 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 1239 kBChecksum SHA-512
98eac60226eb87a5f6eef2f05279907acfd0a92214e9379ca75ffbf9240ba2b6e7c0473a8725aada21f858ccd28e284ea4a702394e996f9565ed6f1fca76b234
Type fulltextMimetype application/pdf

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 19 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 79 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf