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Two-stage deep learning model for automate detection and classification of lung diseases

  • Data analytics and machine learning
  • Published:
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Abstract

Around the world, lung disease is a prevalent cause of death and illness. In this article, we propose a lung disease detection system for the automated identification of critical lung diseases: 1. Tuberculosis 2. Regular Pneumonia 3. COVID Pneumonia and healthy individuals from chest X-ray (CXR) images. The proposed system incorporates a two-stage classification method using an ensemble of convolutional neural networks (CNNs). We evaluated the performance of the proposed system using various evaluation metrics with a test set containing 200 normal, 100 Tuberculosis, 200 Regular Pneumonia, and 100 COVID Pneumonia (a total of 600) CXR images. The proposed system achieves an average macro precision, recall, and F1-score of 0.99 and an accuracy of 0.99. The performance of the proposed system based on F1-Score (0.99) for three classes, namely Tuberculosis, Pneumonia, and COVID, is better than the following existing classification frameworks: ensemble of K-ResNets for the same three classes (F1-Score: 0.84), modified ResNet50 (F1-Score:0.89), KNN classifier with fractional multi-channel exponent moments (F1-Score: 0.89), VGG-16 (F1-Score: 0.89), and convolutional sparse support estimator network (F1-Score for COVID: 0.78). The proposed system can also determine the confidence associated with the challenging classification of Regular Vs. COVID Pneumonia. We also utilized visualization techniques like Grad-CAM and Saliency maps to analyze the features learned by the CNNs, to create transparency in the prediction.

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Data Availability

The data and supportive information are available from the authors upon request.

Notes

  1. https://mmcheng.net/tb/.

  2. https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/overview.

  3. https://github.com/ieee8023/covid-chestxray-dataset.

  4. https://scikit-learn.org.

  5. https://opencv.org/.

  6. https://jupyter.org/.

  7. https://keras.io/.

  8. https://www.tensorflow.org/.

Abbreviations

CXR :

Chest X-ray

CNNs :

Convolutional neural networks

GGOs :

Ground glass opacities

AP :

Antero–posterior

PA :

Posterior–anterior

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Authors and Affiliations

  1. Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa, Vidyapeetham, India

    M. Ganeshkumar, V. Sowmya, E. A. Gopalakrishnan & K. P. Soman

  2. Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia

    Vinayakumar Ravi

  3. Institute of Neurology, Madras Medical College, Chennai, India

    M. Rupeshkumar

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  1. M. Ganeshkumar
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  2. Vinayakumar Ravi
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  3. V. Sowmya
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  4. E. A. Gopalakrishnan
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  5. K. P. Soman
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Correspondence to Vinayakumar Ravi.

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Ganeshkumar, M., Ravi, V., Sowmya, V. et al. Two-stage deep learning model for automate detection and classification of lung diseases. Soft Comput 27, 15563–15579 (2023). https://doi.org/10.1007/s00500-023-09167-9

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