Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Anthimopoulos, Marios; Christodoulidis, Stergios; Ebner, Lukas Michael; Christe, Andreas; Mougiakakou, Stavroula (2016). Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE transactions on medical imaging, 35(5), pp. 1207-1216. Institute of Electrical and Electronics Engineers IEEE 10.1109/TMI.2016.2535865

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Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Diabetes Technology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Anthimopoulos, Marios; Christodoulidis, Stergios; Ebner, Lukas Michael; Christe, Andreas and Mougiakakou, Stavroula

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISSN:

0278-0062

Publisher:

Institute of Electrical and Electronics Engineers IEEE

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

11 May 2016 15:25

Last Modified:

11 May 2016 15:25

Publisher DOI:

10.1109/TMI.2016.2535865

BORIS DOI:

10.7892/boris.80149

URI:

https://boris.unibe.ch/id/eprint/80149

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