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Determination of COPD severity from chest CT images using deep transfer learning network

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Abstract

The purpose of this study is to present a solution to the problem of detecting the severity of Chronic Obstructive Pulmonary Disease (COPD) from chest CT images using deep transfer learning network. The study has a novelty in terms of classifying the severity of COPD with machine learning methods for the first time in the literature. Transfer learning has been preferred because of its proven performance in image analysis and classification. In this study, a dataset containing a total of 1815 CT images from 121 patients with moderate, severe and very severe COPD was used. Lung parenchyma was first segmented from CT images using HSV color space thresholding. Then Inception-V3 model was trained and tested on the segmented image dataset for COPD severity classification. The tests were repeated 10 times. The proposed model was able to detect the severity level of COPD with an average accuracy of 96.79% and a maximum of 97.98%. The classification result proved that the severity of COPD can be classified with very high performance. Thus, the applied transfer learning is promising in medical sciences and can assist to radiologists in making quick and accurate decisions.

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Data Availability

Dataset was taken from Sivas Cumhuriyet University Training and Research Hospital and corporate permission is required for data sharing.

Code Availability

Computer codes are publicly available on https://drive.google.com/drive/folders/12JTXMbPKjyX4gOq6a4TBO1NYb8SvXBs5?usp=sharing

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Funding

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Authors and Affiliations

  1. Department of Mechatronics Engineering, Faculty of Technology, Sivas Cumhuriyet University, Sivas, Turkey

    Özlem Polat

  2. Department of Radiology, Faculty of Medicine, Sivas Cumhuriyet University, Sivas, Turkey

    İsmail Şalk

  3. Department of Chest Diseases, Faculty of Medicine, Sivas Cumhuriyet University, Sivas, Turkey

    Ömer Tamer Doğan

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  1. Özlem Polat
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  2. İsmail Şalk
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  3. Ömer Tamer Doğan
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Contributions

All authors conceived the presented idea. I. Salk and O.T. Dogan prepared collected the dataset and labelled the images. Ö. Polat designed the model and performed the experiments. All authors wrote the manuscript

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Correspondence to Özlem Polat.

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Polat, Ö., Şalk, İ. & Doğan, Ö.T. Determination of COPD severity from chest CT images using deep transfer learning network. Multimed Tools Appl 81, 21903–21917 (2022). https://doi.org/10.1007/s11042-022-12801-7

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  • DOI: https://doi.org/10.1007/s11042-022-12801-7

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