Jul 2026· Journal of the Kenya National Commission for UNESCO· 0 citations· 3 references
TL;DR
The proposed deep learning models provide an efficient and accurate tool for multiclass pneumonia detection from CXR images and have the potential to support healthcare professionals in making more accurate diagnoses.
Abstract
Pneumonia is a significant cause of mortality, particularly in children under five. Accurate detection of pneumonia from Chest X-ray (CXR) images is crucial in mitigating diagnostic errors common in manual radiographic analysis. This study leverages deep learning models to enhance the detection of multiclass pneumonia (normal, bacterial, and viral) using CXR images. We utilized a dataset comprising 5,863 multiclass pneumonia CXR samples. Data augmentation and regularization techniques were applied to address class imbalance and overfitting. Pre-trained models, including EfficientNet, MobileNet, RegNet, and ViT, were fine-tuned using the PyTorch framework, with transfer learning employed to optimize training. Model performance was assessed using accuracy, precision, recall, and specificity. The fine-tuned models achieved high classification accuracy, with EfficientNet and ConvNext models achieving accuracy scores of 83% and 82%, respectively. Data augmentation and regularization significantly improved the models' generalization, reducing overfitting and improving predictive accuracy. The proposed deep learning models provide an efficient and accurate tool for multiclass pneumonia detection from CXRs. These models have the potential to support healthcare professionals in making more accurate diagnoses.
A Hybrid Prototype Fusion Network (HPFN) developed based on the ConvNeXt-Tiny architecture is proposed, and deep feature representation and prototype-based similarity calculation are combined in a single decision mechanism, outperforming both the compared deep learning architectures and traditional feature-based methods.
Pneumonia screening from chest X-ray (CXR) images is clinically important but often suffers from limited data and class imbalance in public benchmarks. This work develops a transfer-learning-based binary classifier using ResNet50 pretrained on ImageNet. Images are resized to 224×224 and normalized to [0,1]. Online augmentation (rotation, zoom, and shifts) is applied during training. To mitigate imbalance, class weights computed from the training labels are used in the loss. We fine-tune the network by freezing the early layers and updating higher-level layers with a small learning rate (Adam, 1e- 4) and early stopping. On the held-out test set (624 images), the model achieves about 0.88 accuracy. For the pneumonia class, precision, recall, and F1-score reach approximately 0.93, 0.88, and 0.90, respectively. The results indicate that combining class-weighted training with fine-tuned ResNet50 provides a practical baseline for automated pneumonia screening on CXR images.
Danning Dong· International Conference on...· 0 citations
Background: Deep learning models, particularly convolutional neural networks (CNNs), have shown promising performance for pneumonia detection using chest X-ray images. However, the impact of preprocessing, architecture selection, data augmentation, and ensemble strategies has not been systematically evaluated. This study investigated how these factors affect model robustness and diagnostic performance. Methods: A public pediatric chest X-ray dataset was used to systematically evaluate pixel normalization methods, six CNN architectures, progressive data augmentation strategies for class imbalance, and both feature-level and decision-level ensemble approaches. Model performance was assessed by considering not only overall classification accuracy but also clinically relevant risk metrics, particularly false-negative rates. Results: Pixel normalization to the 0–1 range improved model convergence, while Xception and InceptionV3 achieved the best overall performance. Model-specific augmentation strategies were more effective than a fixed 1:1 class ratio for reducing false negatives. Feature-level ensembles tended to overfit, whereas decision-level ensembles provided more stable but only modest performance improvements. Conclusions: These findings demonstrate that reliable medical AI systems require systematic optimization of preprocessing techniques, model architecture, data augmentation strategies, and clinically meaningful evaluation metrics rather than maximizing a single performance indicator. The proposed framework provides practical guidelines for developing robust deep learning models for pneumonia diagnosis in clinical settings.
YongJun Kim, Ji-Yeoun Lee· BioMedInformatics· 0 citations
: Pneumonia continues to be a major source of morbidity and mortality globally, especially in developing countries where a shortage of specialists makes radiological assessment challenging. Patients' survival and appropriate treatment depend on a timely and accurate diagnosis. This study examines a deep learning technique called a Convolutional Neural Network (CNN) that looks at chest X-rays to identify pneumonia. To accurately classify normal and pneumonia-infected lungs, the proposed system makes use of sophisticated picture preprocessing, data augmentation, and improved CNNs to find discriminative spatial characteristics. Using pre-trained models like VGG16, ResNet50, and DenseNet121, it also investigated transfer learning to improve model performance and generalization with little datasets. The CNN model demonstrated 96.8 accuracy, 95.6 precision, 97.3 recall, and 96.4 F1-score on the publicly available Chest X-Ray (Pneumonia) dataset. The results suggest that CNN-based models have the capacity to aid radiologists in the early diagnosis to ease the medical intervention and minimize diagnostic errors. This study is part of the developing body of AI-based healthcare diagnostics, and it highlights the possibility of deep learning in medical image processing and disease prediction.
M. Devi, Tanya, Aradhya Mittal et al.· Proceedings of the 1st Inter...· 0 citations
Abstract Pneumonia diagnosis via chest X-rays is critical for effective clinical intervention, yet conventional approaches often fail to capture subtle pathologies. This article introduces a new Pneumonia Multi-Scale Attention Network (PMSAN). This deep learning framework synergistically integrates multiscale feature extraction, channel-wise attention mechanisms, and a novel edge-aware loss (EAL) function to improve pneumonia classification accuracy and interpretability. Extensive experiments on a Kermany pediatric chest X-ray data set (5,856 images) demonstrate that PMSAN, when trained with the EAL, achieves a test set accuracy of 96.4%, precision of 97.9%, recall of 97.2%, F1-score of 97.6%, and an area under the curve (AUC) of 0.988. In addition, fivefold cross-validation shows consistent performance with an accuracy of 96.4% ± 0.5% and AUC of 0.988 ± 0.003, outperforming baseline models such as ResNet18 and VGG16. The model's enhanced interpretability is supported by visualizations including receiver operating characteristic curves, attention maps, and edge maps.
M. R. Mahdiani· Indian Journal of Radiology...· 0 citations
Pneumonia remains one of the leading causes of morbidity and mortality worldwide, particularly among children, older adults, and immunocompromised individuals. Although chest X-ray (CXR) imaging is widely used for pneumonia diagnosis, manual interpretation is time-consuming, subjective, and highly dependent on radiologist expertise. Deep learning has shown promising performance for automated pneumonia classification; however, class imbalance remains a major challenge that can lead to biased predictions and reduced model generalization. Therefore, this study investigates the effectiveness of image augmentation and class weighting for handling class imbalance in pneumonia classification using chest X-ray images. The main contribution of this study is a systematic comparison of four experimental scenarios: Baseline, Augmentation Only, Class Weighting Only, and Hybrid (Image Augmentation and Class Weighting) implemented on a ResNet50 architecture integrated with the Convolutional Block Attention Module (CBAM). Experiments were conducted using the publicly available Chest X-Ray Images (Pneumonia) dataset from Kaggle, comprising 1,583 Normal and 4,273 Pneumonia images. Model performance was evaluated using Accuracy, ROC-AUC, Precision, Recall, F1-Score, confusion matrix analysis, and Youden’s J-Statistic for threshold optimization. The Hybrid model achieved the best overall performance, with an Accuracy of 95.74%, a ROC-AUC of 98.83%, a Macro Precision of 96.01%, a Macro Recall of 93.13%, and a Macro F1-Score of 94.44%. Moreover, the number of false negative predictions decreased from 25 in the Baseline model to 5 in the Hybrid model. These findings demonstrate that integrating image augmentation and class weighting within the ResNet50-CBAM framework effectively mitigates class imbalance and improves the reliability of automated pneumonia classification.
Kafilah Akhmad Fatahillah, T. H. Saragih, D. Kartini et al.· Indonesian Journal of Electr...· 0 citations
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