Related Experiment Video
Updated: Aug 24, 2025
Experimental Model to Evaluate Resolution of Pneumonia
Published on: February 17, 2023
Pediatric pneumonia diagnosis using stacked ensemble learning on multi-model deep CNN architectures
J Arun Prakash1, C R Asswin1, Vinayakumar Ravi2
1Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India.
Insights
A new computer-aided diagnosis model enhances chest X-rays for pediatric pneumonia detection. This model achieves high accuracy, aiding in the early and reliable diagnosis of this acute respiratory infection in children.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Pediatrics
Background:
- Pediatric pneumonia presents a significant global health challenge with high mortality rates.
- Chest X-rays are crucial for diagnosis but face limitations in accuracy for children due to low radiation levels.
- Accurate and timely diagnosis of pediatric pneumonia is essential for effective treatment and improved outcomes.
Purpose of the Study:
- To develop and validate an accurate computer-aided diagnosis (CAD) model for pediatric pneumonia.
- To enhance the diagnostic capabilities of chest X-rays using advanced image processing and machine learning techniques.
- To address the challenges in detecting pediatric pneumonia in low-radiation X-ray images.
Main Methods:
- Image enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE).
- Feature extraction from deep learning models (MobileNet, DenseNet121, DenseNet169, DenseNet201).
- A stacking ensemble classifier fusing deep learning features for classification, validated with Stratified K-Fold cross-validation.
Main Results:
- The proposed model achieved high performance metrics: 98.62% accuracy, 98.99% precision, 99.53% recall, and 99.26% F1 score.
- An AUC score of 93.17% was obtained, demonstrating strong discriminative power.
- The stacking classifier effectively integrated features from multiple deep learning architectures.
Conclusions:
- The developed CAD model shows exceptional performance in diagnosing pediatric pneumonia from chest X-rays.
- This approach offers a promising tool for real-time, accurate, and reliable diagnosis of pediatric pneumonia.
- The model has the potential to significantly assist clinicians in managing pediatric pneumonia cases.
Abstract:
Pediatric pneumonia has drawn immense awareness due to the high mortality rates over recent years. The acute respiratory infection caused by bacteria, viruses, or fungi infects the lung region and hinders oxygen transport, making breathing difficult due to inflamed or pus and fluid-filled alveoli. Being non-invasive and painless, chest X-rays are the most common modality for pediatric pneumonia diagnosis. However, the low radiation levels for diagnosis in children make accurate detection challenging. This challenge initiates the need for an unerring computer-aided diagnosis model. Our work proposes Contrast Limited Adaptive Histogram Equalization for image enhancement and a stacking classifier based on the fusion of deep learning-based features for pediatric pneumonia diagnosis. The extracted features from the global average pooling layers of the fine-tuned MobileNet, DenseNet121, DenseNet169, and DenseNet201 are concatenated for the final classification using a stacked ensemble classifier. The stacking classifier uses Support Vector Classifier, Nu-SVC, Logistic Regression, K-Nearest Neighbor, Random Forest Classifier, Gaussian Naïve Bayes, AdaBoost classifier, Bagging Classifier, and Extra-trees Classifier for the first stage, and Nu-SVC as the meta-classifier. The stacking classifier validated using Stratified K-Fold cross-validation achieves an accuracy of 98.62%, precision of 98.99%, recall of 99.53%, F1 score of 99.26%, and an AUC score of 93.17% on the publicly available pediatric pneumonia dataset. We expect this model to greatly help the real-time diagnosis of pediatric pneumonia.
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