# PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography
## 4 Results  
### Model Performance  
The experimental system was configured with high-performance hardware to support computationally demanding tasks. It featured 64 GB of RAM, ensuring smooth multi-tasking and efficient management of large-scale datasets while preventing performance bottlenecks during data-intensive operations. For storage, a 1 TB HDD (hard disk drive) was integrated, ensuring adequate storage capacity for housing extensive datasets, intermediate computational outputs, and experimental results. Processing capabilities were driven by an 11 th-generation Intel Core i7-11850H processor (Intel, Ankara, Turkriye) (base clock speed: 2.50 GHz), delivering the computational power necessary for complex simulations and real-time analytical workloads. To further enhance performance in parallelizable tasks, particularly machine learning workflows, the system incorporated an NVIDIA GeForce RTX 3070 GPU (NVIDIA, Ankara, Turkriye). This graphics processing unit accelerated matrix operations and neural network training through its parallel computing architecture and optimized CUDA cores, significantly reducing processing times for model iteration and data-driven inference tasks. Six proposed well-known methods for identifying and diagnosing pneumonia were thoroughly tested using X-ray images and established evaluation standards. We employed VGG16, ResNet50, InceptionV3, DenseNet121, DenseNet201, MobileNetV2, and VIT, utilizing pretrained models for feature extraction, in conjunction with a sigmoid/Adams classifier. The performance metrics for validation, and testing, including accuracy, precision, recall, and F1-score for recognizing pneumonia, are presented in Table 6 below:  
Throughout all experiments, image enhancement techniques were applied to improve low-intensity contrast, and noise filtering was utilized, resulting in higher classification accuracy compared to the initial dataset. This study focused on the binary classification of pneumonia and healthy individuals using various pre-trained deep learning models, alongside the proposed ensemble model named PELM. Developed by combining InceptionV3, VGG16, ResNet50, and Vision Transformer (ViT), PELM leverages the complementary strengths of both convolutional and transformer-based architectures. Utilizing a weighted averaging strategy, the ensemble effectively balances high precision, sensitivity, and contextual feature extraction. As a result, PELM achieved 96% accuracy, 99% precision, 91% recall, and an F1-score of 0.95, outperforming all individual models. As demonstrated in \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Specificity** & **AUC** \\ \hline PELM & 0.96 & 0.99 & 0.91 & 0.95 & 0.91 & 0.91 \\ (Proposed Model) & 0.95 & 0.95 & 0.85 & 0.90 & 0.77 & 0.81 \\ ResNet201 & 0.93 & 0.92 & 0.81 & 0.86 & 0.73 & 0.77 \\ MobileNetV2 & 0.93 & 0.94 & 0.83 & 0.88 & 0.76 & 0.88 \\ VGG16 & 0.93 & 0.95 & 0.85 & 0.90 & 0.77 & 0.81 \\ InceptionV3 & 0.92 & 0.97 & 0.87 & 0.92 & 0.83 & 0.85 \\ DenseNet121 & 0.92 & 0.97 & 0.88 & 0.92 & 0.82 & 0.85 \\ ViT & 0.92 & 0.97 & 0.88 & 0.92 & 0.82 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A comparison among existing methods and our proposed method’s performances in pneumonia detection. Table 7, these results suggest that PELM surpasses prior studies in all key metrics, reflecting its potential for real-world clinical implementation. The model's strong performance, enhanced by preprocessing and architecture fusion, not only improves diagnostic accuracy but also reduces computational overhead--making it highly suitable for deployment in resource-constrained or time-sensitive clinical environments. Ultimately, PELM represents a reliable, efficient, and cost-effective solution for pneumonia detection from chest X-rays, contributing to faster diagnoses and improved patient outcomes.