Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
An attention-enhanced deep learning framework for clinically accurate pneumonia identification from chest imaging radiology that combines a self-attention mechanism with a pretrained VGG16 backbone is proposed and tested against many cutting-edge convolutional neural network architectures.
Abstract
Pneumonia continues to be a major source of morbidity and mortality worldwide, especially in children, the elderly, and people with impaired immune systems. Due to its low cost and widespread availability, chest imaging radiology is the most often utilised diagnostic modality; yet, accurate interpretation is difficult and heavily dependent on radiologist expertise, which can result in clinically significant missed diagnosis. While deep learning-based methods have demonstrated potential for automated pneumonia detection, many of the models now in use rely on global feature learning, have poor interpretability, and do not adequately address the danger of false negatives. An attention-enhanced deep learning framework for clinically accurate pneumonia identification from chest imaging radiology is proposed in this study.To enhance spatial feature representation and highlight diagnostically significant lung regions, the framework combines a self-attention mechanism with a pretrained VGG16 backbone. To guarantee stable optimisation and efficient task adaptability, a two-stage training approach is used. The suggested model is tested using a single experimental methodology against many cutting-edge convolutional neural network architectures. The suggested framework obtains an accuracy of 96.3%, recall of 98.0%, F1-score of 97.5%, and ROC–AUC of 0.972, according to experimental results. Significantly, compared to the baseline VGG16 model, false-negative predictions are decreased from 27 to 17.
Pneumonia remains a major cause of lung disease globally, and its timely and reliable diagnosis is crucial. Radiology is often used to detect the infection in a chest X-ray, but this process can be laborious and depend on the radiologist’s interpretation. However, recent advances in deep learning techniques have demonstrated high accuracy in automated pneumonia detection, but their "black-box" nature hampers their practical use. Health-care professionals often need to understand the reasons behind predictions to trust the automated system. This study introduces a machine learning-based approach with additional explainable techniques to enhance model performance and explainability in predicting pneumonia. The proposed method uses a convolutional neural network to predict chest X-ray images, and explainability techniques like Grad-CAM and SHAP are used to explain which parts of the image contribute most to the prediction. We test the system on the RSNA Pneumonia Detection Challenge Dataset that includes expert-provided infection labels. The proposed explanation method is evaluated by comparing the model’s predictions with expert annotations. This study demonstrates that our system not only provides accurate classification results but also provides plausible visual explanations that correspond to the desired locations in the chest. This approach can help boost trust in AI-driven medical diagnostic systems and enable their potential deployment in clinical practice.
Pneumonia is a leading cause of infectious disease mortality worldwide, accounting for approximately 2.5 million deaths annually and 15% of deaths in children under five. Chest X-ray imaging remains the primary diagnostic tool, but accurate interpretation requires radiological expertise that is disproportionately concentrated in high-income settings, creating a diagnostic gap where disease burden is highest. Automated deep learning offers a scalable complement to specialist-dependent diagnosis, yet clinical adoption requires both high accuracy and transparent, interpretable reasoning. Convolutional neural networks (CNNs) have shown strong potential for pneumonia detection from chest X-rays, but two barriers impede clinical translation: the interpretability of black-box models and the computational feasibility of large architectures in resource-constrained settings. Explainable AI (XAI) methods such as Grad-CAM, Grad-CAM++, and Score-CAM address the interpretability barrier, yet systematic quantitative comparisons across multiple CNN architectures remain scarce. Furthermore, CNN architectures widely used for medical image classification carry high parameter counts that limit feasibility in resource-constrained settings, motivating architectures that achieve competitive accuracy with substantially fewer parameters. Here we propose a parameter-efficient deep learning framework for pneumonia detection based on transfer learning, evaluated across three CNN architectures representing distinct architectural families: EfficientNet-B0 with fine-tuning (proposed method), ResNet50, and DenseNet121, trained under identical conditions on the Kaggle chest X-ray dataset (5,863 images). Our method achieved 90% classification accuracy, outperforming both baselines while requiring 4.8x fewer parameters than ResNet50. To evaluate explainability, Grad-CAM, Grad-CAM++, and Score-CAM were applied across all three architectures and compared quantitatively using Intersection over Union against manually annotated lung segmentation masks, Insertion score, and Deletion score, with pairwise statistical validation via Wilcoxon signed-rank tests and Bonferroni correction. Findings show that classification accuracy and XAI explanation quality must be evaluated independently, and that the proposed parameter-efficient architecture offers a favorable trade-off for resource-constrained clinical deployment.
B. Mahtabi, E. Nasr-Esfahani, S. Yaraghi· medRxiv· 0 citations
Pulmonary infections, especially pneumonia constitute a major worldwide health burden and need early and accurate diagnosis in order to minimize disease severity and death. Chest radiographs are regularly performed for screening of pulmonary infections; however, the interpretation of the radiographs by manual techniques is time consuming and subject to inter-observer variability, and this has motivated the need for automated and reliable diagnostic systems. In the current work, a hybrid deep feature learning framework for clinical-level differentiation of pulmonary infections from the chest X-ray images is proposed by effectively incorporating local and global feature representations. The proposed model consists of EfficientNet-B3 for extracting deep spatial features and a Vision Transformer to extract long-range contextual dependencies, a feature fusion strategy towards better representation learning. The framework is tested on a publicly available chest X-ray dataset of Normal and Pneumonia classes. Experimental results show the high classification accuracy of the proposed hybrid model is 98.3% with high precision, recall, F1-score, and AUC performance compared with traditional CNN, ResNet, auto-encoder and transformer-based model. The results underscore the clinical reliability, good generalization capacity and possible applicability of the proposed framework to automated pulmonary infection screening and decision support systems.
T. Srinivas, Faaleha Heba Fakruddin, Mettu Jhansi Rani et al.· 2026 International Conferenc...· 0 citations
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.
Timothy Karani, Stephen Waithaka· Journal of the Kenya Nationa...· 0 citations
Pneumonia is a leading cause of childhood mortality worldwide, and automated detection from chest X-ray images using deep learning holds strong clinical promise. However, model performance is critically sensitive to hyperparameter selection, and existing single-strategy optimization methods are insufficient for complex medical imaging tasks. This paper proposes the Multi-Stage Adaptive Hyperparameter Optimization (MASAHO) framework, which sequentially employs random search, Bayesian optimization, genetic algorithms, and reinforcement learning with principled stage-transition criteria. The framework incorporates an adaptive model selection mechanism based on quantified dataset characteristics and a confidence-weighted clinical loss function that explicitly penalizes false negatives. Experiments on three publicly available datasets—CXR14 [21], RSNA [22], and COVID-19/Pneumonia [23]—demonstrate 97.5% accuracy, 96.2% sensitivity, and 98.8% specificity o n C XR14, outperforming single-strategy baselines, AutoML, Neural Architecture Search, and state-of-the-art domain methods while reducing optimization time by 83% compared to grid search. All reported improvements are statistically confirmed (p < 0.01, Bonferroni-corrected paired t-tests).
Wilny Wilson P, J. D. Dorathi Jayaseeli· 2026 International Conferenc...· 0 citations
A deep learning-based system that uses chest X-ray pictures to automatically detect tuberculosis, using transfer learning using MobileNetV2 and DenseNet architectures to classify chest Xrays as either TB-positive or Healthy, reaching notable accuracy.
Zoya Nasreen, Afshan Fatima, Ruqiya Fatima· International Journal of AI...· 0 citations
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