Cascade of convolutional neural networks for lung texture classification: overcoming ontological overlapping

Sebastian Roberto Tarando; Catalin Fetita; Pierre-Yves Brillet

doi:10.1117/12.2255552

3 March 2017 Cascade of convolutional neural networks for lung texture classification: overcoming ontological overlapping

Sebastian Roberto Tarando, Catalin Fetita, Pierre-Yves Brillet

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 1013407 (2017) https://doi.org/10.1117/12.2255552
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

The infiltrative lung diseases are a class of irreversible, non-neoplastic lung pathologies requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. Traditionally, such classification relies on a two-dimensional analysis of axial CT images. This paper proposes a cascade of the existing CNN based CAD system, specifically tuned-up. The advantage of using a deep learning approach is a better regularization of the classification output. In a preliminary evaluation, the combined approach was tested on a 13 patient database of various lung pathologies, showing an increase of 10% in True Positive Rate (TPR) with respect to the best suited state of the art CNN for this task.

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Sebastian Roberto Tarando, Catalin Fetita, and Pierre-Yves Brillet "Cascade of convolutional neural networks for lung texture classification: overcoming ontological overlapping", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013407 (3 March 2017); https://doi.org/10.1117/12.2255552

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