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September 10, 2025Current Medicinal Chemistry

Enhancing InceptionResNet to Diagnose COVID-19 from Medical Images

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Authors

SAShadi AljawarnehIRIndrakshi Ray

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Overview

Enhanced InceptionResNet demonstrates superior performance in COVID-19 diagnosis from medical images, suggesting increased efficiency.

Key Points

  • The Enhanced InceptionResNet achieved a validation accuracy of 99.0% and testing accuracy of 98.35%, outperforming other models.
  • Key performance metrics such as precision, sensitivity, F1-score, and ROC-AUC indicate the model's superior diagnostic capabilities.
  • The approach utilized a dataset of 2600 X-ray images, allowing for comprehensive model evaluation across relevant parameters.
  • Enhanced InceptionResNet's design incorporates depth-wise separable convolutions, improving feature extraction while minimizing computational costs.

Cite This Study

Aljawarneh et al. (2025) studied this question.

synapsesocial.com/papers/68c1a13354b1d3bfb60dc703https://doi.org/10.2174/0109298673378155250704110629
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Also Consider

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

  1. 1Deep Learning Model for COVID-19 Classification Using Fine Tuned ResNet50 on Chest X-Ray Images2024 · 3 citations
  2. 2Deep 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
  3. 3An Effective ResNet Model for Respiratory Disease Detection: A Case Study on COVID-19 Chest X-ray Images2025
  4. 4A deep convolutional neural network approach using medical image classification2024 · 7 citations
  5. 5Diagnosis of COVID-19 in X-ray Images using Deep Neural Networks2024 · 1 citations