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Multi-View Ensemble Convolutional Neural Network to Improve Classification of Pneumonia in Low Contrast Chest X-Ray

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Summary

    Deep learning models accurately detect pneumonia in pediatric chest X-rays (CXRs), distinguishing between bacterial and viral causes. Pre-processing images significantly improved diagnostic performance, aiding specialists in low-contrast cases.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Pediatric Radiology

    Background:

    • Pneumonia is a major cause of childhood mortality globally.
    • Chest X-rays (CXRs) are crucial for pneumonia diagnosis but can be challenging with low-contrast images.
    • Computational tools, particularly deep learning, can enhance diagnostic accuracy by identifying subtle patterns.

    Purpose of the Study:

    • To develop and evaluate a deep learning model for detecting pneumonia in pediatric CXRs.
    • To differentiate between bacterial and viral pneumonia using the proposed model.
    • To assess the impact of image pre-processing techniques on classification performance.

    Main Methods:

    • Utilized a VGG16 convolutional neural network (CNN) architecture with a customized multilayer perceptron.
    • Implemented four training strategies: original CXR, chest-cavity-cropped image, histogram-equalized segmented image, and an ensemble of cropped and equalized images.
    • Evaluated pre-processing methods including image cropping and histogram equalization.
    • Assessed performance using Area Under the ROC Curve (AUC), accuracy, sensitivity, specificity, and F1-score.

    Main Results:

    • The ensemble model achieved high performance: AUC of 0.97 (95% CI: 0.96-0.99) for pneumonia vs. normal classification.
    • The ensemble model achieved an AUC of 0.91 (95% CI: 0.88-0.94) for bacterial vs. viral pneumonia classification.
    • All models using pre-processed images outperformed the baseline model using original CXRs, with enhanced contrast and detail identification.

    Conclusions:

    • Deep learning models, especially ensemble approaches with pre-processing, significantly improve pneumonia detection and subtyping from pediatric CXRs.
    • Image pre-processing techniques like cropping and histogram equalization are vital for enhancing contrast and revealing subtle radiographic patterns.
    • This approach can aid clinicians in diagnosing pneumonia and its etiological cause more effectively, potentially accelerating treatment decisions.