Machine learning and deep learning approaches for classification of sub-cm lung nodules in CT scans (Conference Presentation)

Rohan Abraham; Ian Janzen; Saeed Seyyedi; Sukhinder Khattra; John Mayo; Ren Yuan; Renelle Myers; Stephen Lam; Calum E. MacAulay

doi:10.1117/12.2546422

17 March 2020 Machine learning and deep learning approaches for classification of sub-cm lung nodules in CT scans (Conference Presentation)

Rohan Abraham, Ian Janzen, Saeed Seyyedi, Sukhinder Khattra, John Mayo, Ren Yuan, Renelle Myers, Stephen Lam, Calum E. MacAulay

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113141G (2020) https://doi.org/10.1117/12.2546422
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

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Rohan Abraham, Ian Janzen, Saeed Seyyedi, Sukhinder Khattra, John Mayo, Ren Yuan, Renelle Myers, Stephen Lam, and Calum E. MacAulay "Machine learning and deep learning approaches for classification of sub-cm lung nodules in CT scans (Conference Presentation)", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141G (17 March 2020); https://doi.org/10.1117/12.2546422

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