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Explainable Lung Disease Detection on Chest X-Ray Images Using Gradient-Weighted Class Activation Mapping

Benny Sukma Negara, Muhammad Irsyad, Wan Sofiyah

2026enlung diseasechest x-raycovid-19pneumoniadeep learningexplainable ai

Abstract

Language:

Early detection of respiratory diseases such as COVID-19 and pneumonia is essential to speed up treatment and prevent severe complications. This study develops a chest X-ray image classification system using a Convolutional Neural Network (CNN), specifically a VGG16-based architecture, to distinguish between COVID-19, pneumonia, and normal lungs. The model is trained on a balanced dataset of 3,000 images (1,000 per class), with preprocessing steps including resizing, normalization, train–validation splitting, and data augmentation. Hyperparameter exploration identified an optimal configuration with a learning rate of 0.001, 50 epochs, and a batch size of 32, achieving an accuracy of 96.33%. Performance was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. To enhance transparency and interpretability in a clinical context, Gradient-Weighted Class Activation Mapping (Grad-CAM) was applied to visualize the regions of the CXR images that contributed most to the model’s decisions. The main contribution lies in integrating Grad-CAM with multi-class CXR classification to provide explainable AI support for lung disease diagnosis.

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Cite This Work

@article{f0c123fd-b450-4714-b0d2-4ac7959226fa,
  title={Explainable Lung Disease Detection on Chest X-Ray Images Using  Gradient-Weighted Class Activation Mapping},
  author={Benny Sukma Negara and Muhammad Irsyad and Wan Sofiyah},
  year={2026},
  language={en}
}
TY  - JOUR
TI  - Explainable Lung Disease Detection on Chest X-Ray Images Using  Gradient-Weighted Class Activation Mapping
AU  - Benny Sukma Negara
AU  - Muhammad Irsyad
AU  - Wan Sofiyah
PY  - 2026
LA  - en
ER  -

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