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2000
Volume 18, Issue 7
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Introduction

The medical field can utilize radiological images with deep learning techniques to diagnose disease more accurately, enabling the diagnosis and classification of a variety of illnesses. In the domain of learning and machine vision, identifying COVID-19 from X-ray images is a developing area. Since the onset of COVID-19, significant work has been performed, yet some issues remain in this field.

Methods

Firstly, there are limited X-ray scans readily available that are classified as COVID-19 positive, resulting in an unbalanced dataset. Secondly, there is no single set of data, classes, or evaluation protocols for all the work performed. This study proposes a three-class balanced dataset based on two validated publicly available datasets. Deep Convolutional neural networks have the potential to operate with both wide breadth and wide depth, which could raise computing complexity. Additionally, to deal with this issue, an attention-guided ensemble model (AGEM) is proposed to classify normal, pneumonia, and COVID-19 images. First, we propose an Attention Guided-Convolutional Neural Network (AG-CNN) architecture based on transfer learning. We used three pre-trained models , InceptionV3, DenseNet121, and MobileNetV2, as the basis for the proposed AG-CNN, resulting in three attention-guided network architectures , AG-InceptionV3, AG-DenseNet121, and AG-MobileNetV2. Then, we used entropy computation and an uncertainty-based weighting ensemble to classify the images into three classes.

Results

The performance was evaluated and compared with existing works and 7 pre-trained models , ResNet50, InceptionV3, VGG-16, VGG-19, Densenet-201, Xception, MobileNetV2, on our three-class dataset. An accuracy of 97.35%, recall of 97.35%, specificity of 98.67%, precision of 97.35%, and F1-score of 97.35% demonstrate the superiority of our proposed attention-guided ensemble model over pre-trained models and other existing studies.

Conclusion

It is noteworthy that for additional analysis, we utilized Grad-CAM or gradient-weighted Class Activation Mapping.

© 2025 Bentham Science Publishers
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/content/journals/raeeng/10.2174/0123520965334135241115064754
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(XAI-AGUWEM) Explainable Artificial Intelligence-based Attention Guided Uncertainty Weighting Ensemble Model for the Classification of COVID-19 and Pneumonia in X-ray Medical Images
Recent Advances in Electrical & Electronic Engineering 18, 862 (2025); https://doi.org/10.2174/0123520965334135241115064754
/content/journals/raeeng/10.2174/0123520965334135241115064754
/content/journals/raeeng/10.2174/0123520965334135241115064754
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  • Article Type:     Research Article
Keyword(s): Attention mechanism; CNN; COVID-19; ensemble; entropy; pneumonia; transfer learning; uncertainty weighting

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