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https://doi.org/10.62836/iaet.vli1.002
Li, S., Mo, Y., & Li, Z. (2022). Automated Pneumonia Detection in Chest X-Ray Images Using Deep Learning Model. Innovations in Applied Engineering and Technology, 1(1), 1–6. https://doi.org/10.62836/iaet.vli1.002
Volume 1, Issue 1

Automated Pneumonia Detection in Chest X-Ray Images Using Deep Learning Model

In the medical field, especially for the rapid diagnosis of pneumonia, enhancements in accuracy and efficiency are crucial for patient treatment and recovery. Traditional analysis of chest X-ray images relies on the professional judgment of radiologists, which can be time-consuming and may vary with the doctor’s level of experience. With the rapid development of deep learning technology, particularly the widespread application of Convolutional Neural Networks (CNNs) in image processing, we now have the opportunity to use these advanced technologies to automate the process of diagnosing pneumonia. This study has constructed a deep learning model using the TensorFlow and Keras frameworks, aiming to automatically detect pneumonia from chest X-ray images. The construction process of the model involved complex data preprocessing, model design, and parameter tuning. Our model utilizes multiple layers of convolutional networks to capture image features and employs fully connected layers for classification. After rigorous training and validation, our model achieved an accuracy of 97% on the test set. This result demonstrates the effectiveness of the model in identifying pneumonia. Such improved performance offers a powerful tool for physicians, promising to significantly increase the speed and consistency of pneumonia diagnosis, ultimately enhancing patient treatment outcomes.

medical diagnosis; pneumonia; deep learning; convolutional neural networks; tensorflow; sequential model

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Supporting Agencies

  1. Funding: Not applicable.