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September 10, 2025Artificial Intelligence and Applications

An Effective ResNet Model for Respiratory Disease Detection: A Case Study on COVID-19 Chest X-ray Images

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Authors

BHBushra HassanKKKaveh KianiTMTaha Mansouri

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Overview

Analysis demonstrates high accuracy of ResNet models in identifying COVID-19, highlighting diagnostic advancements with AI.

Key Points

  • ResNet101 achieved 93% accuracy in detecting COVID-19 from chest X-ray images, outperforming other models.
  • Precision metrics were also impressive, with ResNet101 showing 91.27% precision and an AUC–ROC of 97.41%.
  • The study utilized deep convolutional neural networks, comparing ResNet with VGG and AlexNet models for respiratory disease detection.
  • Current limitations in AI for medical imaging include small datasets and insufficient clinical validation for reliability.

Cite This Study

Hassan et al. (2025) studied this question.

synapsesocial.com/papers/68c1d5ef54b1d3bfb60f8b89https://doi.org/10.47852/bonviewaia52024885
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1AI-Powered Diagnosis of Respiratory Diseases: A CNN-Based Study on Chest X-ray Image Classification2026
  2. 2Deep Learning Model for COVID-19 Classification Using Fine Tuned ResNet50 on Chest X-Ray Images2024 · 3 citations
  3. 3Deep Learning Methods for Chest X-Ray Imaging-Based COVID-19 Pneumonia Detection2024 · 1 citations
  4. 4Deep Learning Approaches for COVID-19 Detection from CT Scans and Chest X-Rays: A Comparative Study of VGG, ResNet, Inception, and Xception Models2024 · 1 citations
  5. 5Comparative Analysis of VGG16, RESNET50, AND CNN Models for Lung Disease Prediction: A Deep Learning Approach2024