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A Deep Learning Method for Pneumonia Detection Based on Fuzzy Non-Maximum Suppression.

Hongli Wu, Huijuan Lu, Mingzhu Ping

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    This study introduces a deep learning method for detecting pneumonia in X-rays, improving accuracy by using Res2Net and a novel Fuzzy Non-Maximum Suppression (FNMS) algorithm for better feature extraction and box fusion.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Pneumonia remains a leading global cause of mortality.
    • Deep learning aids in detecting pneumonia from chest X-rays.
    • Existing methods struggle with scale variation and blurred boundaries of pneumonia regions.

    Purpose of the Study:

    • To develop an advanced deep learning method for accurate pneumonia detection in chest X-rays.
    • To address limitations in handling scale variations and blurred boundaries of pneumonia areas.

    Main Methods:

    • Integration of Res2Net into the RetinaNet architecture for enhanced multi-scale feature extraction.
    • Proposal of a novel Fuzzy Non-Maximum Suppression (FNMS) algorithm for robust fusion of overlapping detection boxes.
    • Ensemble of models with different backbones for improved performance.

    Main Results:

    • The proposed RetinaNet with Res2Net backbone and FNMS algorithm outperformed existing methods in single-model evaluations.
    • The FNMS algorithm demonstrated superior performance in fusing predicted boxes compared to NMS, Soft-NMS, and weighted boxes fusion in model ensembles.
    • Experimental results validated the effectiveness of the FNMS algorithm and the overall method for pneumonia detection.

    Conclusions:

    • The developed deep learning approach significantly enhances pneumonia detection accuracy.
    • The FNMS algorithm offers a robust solution for object detection box fusion, particularly in medical imaging.
    • This method shows great promise for improving diagnostic capabilities in pneumonia detection.