Data-Driven Clinical Decision Support Framework for Automated Pneumonia Detection

Abstract

Pneumonia remains one of the leading respiratory diseases responsible for high mortality worldwide, particularly among children, elderly individuals, and immunocompromised patients. Chest X-ray imaging is widely used for diagnosis because of its affordability and accessibility; however, manual interpretation is time-consuming and highly dependent on radi-ological expertise. Recent advancements in Artificial Intelligence (AI) and deep learning have enabled automated pneumonia detection systems capable of assisting clinicians in medical image analysis. This paper presents a comprehensive survey and a proposed Data-Driven Clinical Decision Support Framework for auto-mated pneumonia detection using chest X-ray images. Existing Convolutional Neural Network (CNN) architectures demonstrate strong local feature extraction capabilities but often fail to capture long-range contextual dependencies. Transformer-based architectures improve contextual understanding but introduce high computational complexity and increased dataset require-ments. Recent hybrid CNN–Transformer frameworks attempt to combine both local and global feature learning for improved diagnostic performance. Based on the identified research gaps, a hybrid framework integrating DenseNet, Swin Transformer, and CBAM attention mechanisms is proposed. The framework also incorporates Ex-plainable Artificial Intelligence (XAI) techniques such as Grad-CAM and LIME to improve interpretability and physician trust. Furthermore, a Large Language Model (LLM)-based reporting module is introduced to generate automated clinical summaries and diagnostic explanations. The proposed framework aims to improve prediction accuracy and contextual understanding while enhancing interpretability and clinical assistance. The work focuses on integrating hybrid deep learning, Explainable AI, and LLM-based clinical reasoning into a unified clinical decision support framework.

Introduction

The text focuses on pneumonia detection using chest X-ray images and how modern AI techniques are improving diagnosis. Pneumonia is a serious lung infection with high mortality risk, especially when diagnosis is delayed. Chest X-rays are widely used for detection, but manual interpretation can be inconsistent and limited by lack of expert radiologists.

To address this, the study reviews and proposes AI-based solutions using deep learning. CNN models like DenseNet, VGG16, and EfficientNet are effective in learning local image features, while attention mechanisms (CBAM, SE) improve focus on important lung regions. However, CNNs struggle to capture global context in images.

Transformer-based models such as Vision Transformers and Swin Transformers overcome this limitation by learning long-range relationships, but they require high computational resources and large datasets. As a result, hybrid CNN–Transformer models are increasingly used to combine local feature extraction with global contextual understanding.

The proposed framework integrates DenseNet, Swin Transformer, CBAM attention, Explainable AI (Grad-CAM, LIME), and Large Language Models to create a complete clinical decision support system. It not only classifies pneumonia but also highlights affected lung regions and generates automated clinical reports.

The literature review shows that earlier approaches focused mainly on classification accuracy, while newer methods emphasize interpretability, attention mechanisms, and hybrid architectures. Despite progress, challenges remain in computational cost, data requirements, and real-world deployment.

Conclusion

This paper presented a comprehensive survey and proposed framework for a Data-Driven Clinical Decision Support Sys-tem for automated pneumonia detection using chest X-ray images. The survey analyzed CNN-based architectures, attention mechanisms, Vision Transformers, Explainable AI tech-niques, and hybrid CNN–Transformer models. Existing studies demonstrated that CNNs provide strong local feature ex-traction capabilities while transformer architectures improve contextual understanding. Based on the identified research gaps, a hybrid framework integrating DenseNet, Swin Transformer, CBAM attention mechanisms, Explainable AI techniques, and LLM-based re-porting was proposed. The proposed framework aims to improve classification ac-curacy, contextual understanding, interpretability, and clinical usability within a unified healthcare-oriented system. Future work will focus on model evaluation, lightweight deployment, multimodal integration, advanced Explainable AI methods, and real-world clinical validation.

References

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Copyright

Copyright © 2026 Tejonidhi M R, Nandan H S, Poorab N G, Kushal H S, Poornachandra N. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.