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Proceedings Paper

Machine learning and deep learning approaches for classification of sub-cm lung nodules in CT scans (Conference Presentation)
Author(s): Rohan Abraham; Ian Janzen; Saeed Seyyedi; Sukhinder Khattra; John Mayo; Ren Yuan; Renelle Myers; Stephen Lam; Calum E. MacAulay

Paper Abstract

Lung Cancer screening trials have demonstrated significant mortality reduction. Low-Dose Computed Tomography (LDCT) screening can frequently discover many small nodules in at risk participants. However classification of these, sub-cm nodules as cancerous or benign is a challenging task even for expert clinicians. We use machine learning (ML) and deep learning (CNN) techniques to differentiate, sub-cm cancerous and benign nodules. Data for this study is drawn from a screening study (PanCan) from which we selected 612 distinct nodules (140 cancerous, and ~size matched 472 benign). Both methods demonstrated a ~80% accuracy, whereas currently used measures (size) had a 68% accuracy.

Paper Details

Date Published: 17 March 2020
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141G (17 March 2020); doi: 10.1117/12.2546422
Show Author Affiliations
Rohan Abraham, BC Cancer Research Ctr. (Canada)
Ian Janzen, BC Cancer Research Ctr. (Canada)
Saeed Seyyedi, BC Cancer Research Ctr. (Canada)
Sukhinder Khattra, BC Cancer Research Ctr. (Canada)
John Mayo, BC Cancer Research Ctr. (Canada)
Ren Yuan, BC Cancer Research Ctr. (Canada)
Renelle Myers, BC Cancer Research Ctr. (Canada)
Stephen Lam, BC Cancer Research Ctr. (Canada)
Calum E. MacAulay, BC Cancer Research Ctr. (Canada)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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