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<p>COVID-19 is a contagious disease caused by SARS-CoV-2 infection. Chest X-ray (CXR) imaging provides a faster alternative for COVID-19 screening. However, manual interpretation is subjective and prone to errors. Deep learning models can automate COVID-19 detection with high accuracy. This study proposes an ensemble model combining ResNet50 and MobileNetV2. ResNet50 extracts deep hierarchical features for precise classification. MobileNetV2 enhances efficiency while maintaining strong performance. A segmentation step isolates lung regions to improve feature extraction. Grad-CAM enhances explainability by highlighting infection-prone areas. The dataset includes CXR images from the COVID-19 Radiography Database. Images were split 80:10:10 for training, validation, and testing with stratified sampling. The ensemble model integrates feature fusion for improved classification. The model was optimized using Adam optimizer (learning rate: 0.0001, β<sub>1</sub> = 0.9, β<sub>2</sub> = 0.999) for 10 epochs with early stopping (patience = 3, monitoring validation loss). Performance evaluation considers accuracy, sensitivity, specificity. Using stratified fivefold cross-validation the model achieved 97.55% accuracy, precision of 97.2%, F1-score of 97.4% and AUC-ROC of 0.989 in differentiating COVID-19 from normal cases. Grad-CAM heatmaps confirm the model's focus on infection regions. Infection severity is quantified using lung segmentation and Gradient-weighted Class Activation Mapping (Grad-CAM) overlays. Higher severity scores correspond to severe lung involvement in patients. The proposed method enhances COVID-19 detection and interpretability.</p>

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CORE-MONET: A Multi-Stage Ensemble Deep Learning Framework for Automated COVID-19 Detection and Explainable Lung Infection Severity Assessment from Chest X-rays

  • 期刊论文

  • 发表日期：

    2026年5月17日

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  • A. Anix Mary Javitha,
  • Z. Mary Livinsa

摘要

COVID-19 is a contagious disease caused by SARS-CoV-2 infection. Chest X-ray (CXR) imaging provides a faster alternative for COVID-19 screening. However, manual interpretation is subjective and prone to errors. Deep learning models can automate COVID-19 detection with high accuracy. This study proposes an ensemble model combining ResNet50 and MobileNetV2. ResNet50 extracts deep hierarchical features for precise classification. MobileNetV2 enhances efficiency while maintaining strong performance. A segmentation step isolates lung regions to improve feature extraction. Grad-CAM enhances explainability by highlighting infection-prone areas. The dataset includes CXR images from the COVID-19 Radiography Database. Images were split 80:10:10 for training, validation, and testing with stratified sampling. The ensemble model integrates feature fusion for improved classification. The model was optimized using Adam optimizer (learning rate: 0.0001, β1 = 0.9, β2 = 0.999) for 10 epochs with early stopping (patience = 3, monitoring validation loss). Performance evaluation considers accuracy, sensitivity, specificity. Using stratified fivefold cross-validation the model achieved 97.55% accuracy, precision of 97.2%, F1-score of 97.4% and AUC-ROC of 0.989 in differentiating COVID-19 from normal cases. Grad-CAM heatmaps confirm the model's focus on infection regions. Infection severity is quantified using lung segmentation and Gradient-weighted Class Activation Mapping (Grad-CAM) overlays. Higher severity scores correspond to severe lung involvement in patients. The proposed method enhances COVID-19 detection and interpretability.