# PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography
## 2 Literature Reviews  
Over the past decade, deep learning (DL) has shown considerable success in automating the detection of pneumonia from chest radiographs (CXR), addressing challenges of radiologist workload and diagnostic variability. This section analyzes prior studies with an emphasis on their modeling strategies, dataset use, and clinical relevance, forming the foundation for the proposed PELM ensemble framework. Several key studies in the field have laid important groundwork by exploring different architectures, learning paradigms, and evaluation strategies in CXR-based pneumonia classification:* [PERSON] et al. (2015) conducted a performance comparison of several fine-tuned models, notably InceptionResNet_V2, ResNet50, and MobileNet_V2, and found that an ensemble of these achieved the highest F1-score (94.84%) on a COVID-era pneumonia dataset. While effective, their work focused on model-level stacking without optimized weight tuning, limiting adaptability across datasets. * [PERSON] and [PERSON] (2016) used transfer learning with four CNNs--AlexNet, ResNet18, DenseNet201, and SqueezeNet--to distinguish between bacterial and viral pneumonia. Their comprehensive three-class evaluation (normal, bacterial, viral) demonstrated DenseNet201's superiority, yet lacked ensemble integration or spatial attention mechanisms, which are vital for nuanced feature focus. * [PERSON] et al. (2017) incorporated LSTM layers with MobileNet and ResNet backbones to model temporal dependencies in X-ray sequences. Although this hybrid CNN-RNN approach achieved 90.2% accuracy, its application to static CXR data is limited and requires sequential imaging, which is not always clinically available. * [PERSON] et al. (2018) proposed a temporal model using CheXNet and GRUs to track COVID-19 progression across serial CXRs. Their zone-based approach yielded a strong AUC of 0.98, showing the value of spatial granularity, though the model's reliance on longitudinal data limits its generalizability to single-instance diagnoses. * [PERSON] et al. (2019), through the CheXpert dataset, demonstrated DenseNet121's capability to achieve radiologist-level performance (AUC: 0.98). However, the study was confined to single-model evaluations and did not explore ensemble robustness or recall optimization, which are critical in minimizing missed diagnoses. * [PERSON] et al. (2019) introduced multi-head attention and dynamic pooling within a DenseNet121-EfficientNetB0 hybrid, achieving 95.19% accuracy and significantly reducing false positives. Despite strong precision (98.38%), the method's computational complexity and architectural specificity may hinder scalability in resource-constrained settings. * [PERSON] et al. (2018) benchmarked over 60 CNN and ViT models on cervical imaging, illustrating that ViT-B16 combined with ensemble strategies (e.g., max-voting) improved low-class performance. While not focused on pneumonia, this study underlines the value of Transformer-based architectures and voting techniques in medical imaging. * [PERSON] et al. (2020) compared lightweight custom CNNs with ensemble strategies on pediatric CXRs. Their use of SMOTE for class balancing and separable convolutions yielded 95% accuracy in binary classification. However, their ensemble model underperformed compared to their standalone CNN, indicating potential redundancy without adaptive weighting. * [PERSON] and [PERSON] (2018) combined VGG16 feature extraction with multiple classifiers (e.g., NN, SVM, RF), achieving up to 95.4% accuracy and AUCs of 0.988. While showcasing the benefit of hybrid pipelines, the approach lacked architectural fusion, which can consolidate spatial-semantic feature richness. * [PERSON] et al. (2021) demonstrated that combining DenseNet169 features with classical classifiers like SVM outperformed logistic regression and basic CNNs (AUC: 0.80). However, limited dataset size (\(n=2862\)) and absence of attention mechanisms constrained generalizability.