Lung tissue characterization for emphysema differential diagnosis using deep convolutional neural networks

Mohammadreza Negahdar; David Beymer

doi:10.1117/12.2513044

13 March 2019 Lung tissue characterization for emphysema differential diagnosis using deep convolutional neural networks

Mohammadreza Negahdar, David Beymer

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109503R (2019) https://doi.org/10.1117/12.2513044
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

In this study, we propose and validate an end-to-end pipeline based on deep learning for differential diagnosis of emphysema in thoracic CT images. The five lung tissue patterns involved in most differential restrictive and obstructive lung disease diagnoses include: emphysema, ground glass, fibrosis, micronodule, and normal. Four established network architectures have been trained and evaluated. To the best of our knowledge, this is the first comprehensive end-to-end deep CNN pipeline for differential diagnosis of emphysema. A comparative analysis shows the performance of the proposed models on two publicly available datasets.

Citation Download Citation

Mohammadreza Negahdar and David Beymer "Lung tissue characterization for emphysema differential diagnosis using deep convolutional neural networks", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109503R (13 March 2019); https://doi.org/10.1117/12.2513044

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