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  1. Home
  3. A Deep Convolutional Neural Network Model For Lung Disease Detection Using Chest X-ray Imaging.
  1. Home
  3. A Deep Convolutional Neural Network Model For Lung Disease Detection Using Chest X-ray Imaging.

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A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging.

Samia Dardouri1,2

  • 1Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia.

Pulmonary Medicine
|July 2, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

An automated system effectively detects pneumonia and COVID-19 using chest x-rays and CT scans. This deep learning model achieves high accuracy, aiding in early lung disease diagnosis.

Keywords:
Adam optimizerCNNdeep learningfeature extractionimage data augmentationlung disease detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • Lung diseases like pneumonia and COVID-19 pose significant global health challenges.
  • Early and accurate diagnosis is crucial for effective patient management and treatment.
  • Medical imaging, particularly chest radiographs, is a cornerstone for lung disease detection due to accessibility and speed.

Purpose of the Study:

  • To develop an automated system for detecting multiple lung diseases, including pneumonia and COVID-19, in medical scans.
  • To leverage a customized convolutional neural network (CNN) integrated with pretrained models and image enhancement for improved diagnostic accuracy.
  • To evaluate the system's performance on a diverse dataset of chest x-ray and CT images.

Main Methods:

  • Utilized a dataset of 6400 chest x-ray and CT images categorized into pneumonia, COVID-19, and normal classes.
  • Employed data augmentation techniques to address class imbalance within the dataset.
  • Developed a deep learning model incorporating a customized CNN, pretrained models, image enhancement, preprocessing, and classification stages.

    • Main Results:

    • The automated system achieved high performance metrics: 96% precision, 95.33% recall, 95.66% F1-score, and 97.24% accuracy.
    • Demonstrated superior effectiveness compared to other existing deep learning models for lung disease detection.
    • The integrated approach of CNN, pretrained models, and image enhancement proved highly successful.

    Conclusions:

    • The proposed automated system shows significant promise for the accurate and efficient detection of multiple lung diseases.
    • This AI-driven approach can enhance early diagnosis, potentially improving patient outcomes and disease management.
    • The study highlights the potential of customized CNNs and image enhancement in medical diagnostics.