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A Deep Learning Approach Considering Image Background for Pneumonia Identification Using Explainable AI (XAI).

Yuting Yang, Gang Mei, Francesco Piccialli

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    This study introduces a new deep learning approach for identifying pneumonia from X-ray images by removing background noise. This method enhances diagnostic accuracy for earlier pneumonia detection and treatment.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Science

    Background:

    • Pneumonia is a lung infection often diagnosed using X-rays.
    • Current deep learning models for pneumonia detection can be affected by image background noise, limiting accuracy.
    • Explainable AI is crucial for understanding model decisions in medical diagnostics.

    Purpose of the Study:

    • To develop a deep learning method for pneumonia identification that accounts for image background factors.
    • To improve the accuracy of pneumonia detection in lung X-ray images.
    • To enhance model interpretability using explainable deep learning techniques.

    Main Methods:

    • Developed preliminary deep learning models for pneumonia X-ray identification without background consideration.
    • Built improved deep learning models incorporating background factors to enhance pneumonia recognition.
    • Utilized the Grad-CAM method for explainable AI analysis of the models.

    Main Results:

    • The proposed method, considering image background, significantly improved pneumonia identification accuracy.
    • The VGG16 model achieved a highest accuracy of 95.6% using the background-aware approach.
    • Grad-CAM analysis provided insights into the model's decision-making process.

    Conclusions:

    • Accounting for image background in deep learning models enhances pneumonia detection accuracy.
    • The developed explainable AI approach offers a more reliable method for pneumonia diagnosis.
    • This technique holds potential for early pneumonia detection and treatment in clinical settings.