Automatic Childhood Pneumonia Diagnosis Based on Multi-Model Feature Fusion Using Chi-Square Feature Selection.

Area of Science:

• Medical Imaging
• Artificial Intelligence in Healthcare
• Pediatric Diagnostics

Background:

• Childhood pneumonia is a leading cause of mortality, with chest radiography (CXR) crucial for diagnosis.
• Accurate pneumonia detection in pediatric CXR is challenging due to low radiation exposure and limitations of traditional methods.
• Deep learning, specifically Convolutional Neural Networks (CNNs), shows promise in improving medical image analysis.

Purpose of the Study:

• To propose an accurate pneumonia detection method for pediatric chest X-rays.
• To leverage deep CNN architectures and optimal feature fusion for enhanced diagnostic performance.
• To improve the timely detection of childhood pneumonia, particularly in resource-limited healthcare settings.

Main Methods:

• Training enhanced VGG-19, ResNet-50, and MobileNet-V2 models on a large pneumonia dataset using transfer learning.
• Applying the Chi-Square technique to remove irrelevant features from each CNN model.
• Performing horizontal feature fusion of selected features from VGG-19 and MobileNet-V2 for binary classification using a Support Vector Machine (SVM) classifier.

Main Results:

• The proposed fused-feature approach achieved high performance metrics: 97.59% accuracy, 98.33% recall, and 98.19% F1-score on the test set.
• The method demonstrated significant performance enhancement compared to existing ensemble fusion techniques.
• The system ensures computational efficiency while providing robust feature representation.

Conclusions:

• The developed fused-feature deep CNN system offers a significant advancement in the accurate and timely detection of childhood pneumonia.
• This approach holds potential for widespread application, especially in healthcare systems with limited resources.
• The study highlights the effectiveness of combining deep learning models and feature fusion for pediatric diagnostic imaging.