A Hybrid Ensemble Deep Learning Framework for Pediatric Pneumonia Classification Using Transfer Learning and Convolutional Neural Networks

Current implementation of pneumonia diagnosis remains challenging to achieve better performance and improve to get better result. Convolutional neural networks (CNNs) have demonstrated the successful automation of pneumonia diagnosis through the analysis of chest X-ray images, which can be combined with other methods to improve prediction and classification accuracy rates. The aim of this research is to propose an innovative framework for pediatric pneumonia diagnosis that unites three fine-tuned pre-trained CNN models through feature fusion at the EfficientNetB0, RestNet50, and MobileNetV2 to achieve better performance. The mixed-model architecture framework provides an ideal solution for time-sensitive clinical applications operating in resource-constrained environments. The proposed framework model demonstrates successful performance in maintaining excellent sensitivity and specificity measures because clinical use requires minimal false-negative and false-positive results. Furthermore, the proposed framework model outperformed individual models and compared favorably to previous studies related to pneumonia classification, achieving an accuracy level of 96.14%, a precision of 94.10%, a recall of 96.92%, and an F1-score of 94.97%.