# Explainable Deep Learning for Pediatric Pneumonia Detection in Chest X-Ray Images

arXiv: https://arxiv.org/abs/2601.09814 · HTML: https://mlsanity.com/paper/2601.09814/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 14 Jan 2026.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?](https://mlsanity.com/paper/1706.00712/) (2017)
- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [Explainable Deep Learning in Medical Imaging: Brain Tumor and Pneumonia Detection](https://mlsanity.com/paper/2510.21823/) (2025)

## Abstract (arXiv)

Background: Pneumonia remains a leading cause of morbidity and mortality among children worldwide, emphasizing the need for accurate and efficient diagnostic support tools. Deep learning has shown strong potential in medical image analysis, particularly for chest X-ray interpretation. This study compares two state-of-the-art convolutional neural network (CNN) architectures for automated pediatric pneumonia detection. Methods: A publicly available dataset of 5,863 pediatric chest X-ray images was used. Images were preprocessed through normalization, resizing, and data augmentation to enhance generalization. DenseNet121 and EfficientNet-B0 were fine-tuned using pretrained ImageNet weights under identical training settings. Performance was evaluated using accuracy, F1-score, Matthews Correlation Coefficient (MCC), and recall. Model explainability was incorporated using Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-agnostic Explanations (LIME) to visualize image regions influencing predictions. Results: EfficientNet-B0 outperformed DenseNet121, achieving an accuracy of 84.6%, F1-score of 0.8899, and MCC of 0.6849. DenseNet121 achieved 79.7% accuracy, an F1-score of 0.8597, and MCC of 0.5852. Both models demonstrated high recall values above 0.99, indicating strong sensitivity to pneumonia detection. Grad-CAM and LIME visualizations showed consistent focus on clinically relevant lung regions, supporting the reliability of model decisions. Conclusions: EfficientNet-B0 provided a more balanced and computationally efficient performance compared to DenseNet121, making it a strong candidate for clinical deployment. The integration of explainability techniques enhances transparency and trustworthiness in AI-assisted pediatric pneumonia diagnosis.