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Collapsed lung disease classification by coupling denoising algorithms and deep learning techniques

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Abstract

Lung collapse is an adverse lung condition that occurs due to an injury, tumor, or cancer in the lung. Atelectasis and pneumothorax are two primary lung disorders that can lead to the collapse of the lungs. In this article, we aimed to identify the cases of atelectasis and pneumothorax from the X-ray images of human lungs. The X-ray images are enhanced with Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Discrete Wavelet Transform (DWT) separately to remove the noises and improve the image quality. The enhanced images are convolved and merged together before passing them through a modified DenseNet201 pre-trained model. The existing DenseNet201 was supplemented with extra global average pooling and a dense layer. The experimental results on a publicly available dataset achieved a classification accuracy of 97.77%, precision of 96%, recall of 98%, and F1-score of 96%. The proposed model outperforms the existing model with an improvement of 3.9% over the existing state-of-the-art model.

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Data availability

The dataset for the current study is available at: www.kaggle.com/nih-chest-xrays/data.

Notes

  1. https://www.cedars-sinai.org/health-library/diseases-and-conditions/c/collapsed-lung-atelectasis.html.

  2. https://my.clevelandclinic.org/health/diseases/15304-collapsed-lung-pneumothorax.

  3. https://www.singlecare.com/blog/atelectasis-vs-pneumothorax/.

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Authors and Affiliations

  1. Computer Science and Engineering Department, National Institute of Technology Patna, Patna, Bihar, India

    Upasana Chutia, Anand Shanker Tewari & Jyoti Prakash Singh

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  1. Upasana Chutia
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  2. Anand Shanker Tewari
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  3. Jyoti Prakash Singh
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Correspondence to Anand Shanker Tewari.

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Chutia, U., Tewari, A.S. & Singh, J.P. Collapsed lung disease classification by coupling denoising algorithms and deep learning techniques. Netw Model Anal Health Inform Bioinforma 13, 1 (2024). https://doi.org/10.1007/s13721-023-00435-0

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