# A Real-Time DDS-Based Chest X-Ray Decision Support System for Resource-Constrained Clinics

arXiv: https://arxiv.org/abs/2412.07818 · HTML: https://mlsanity.com/paper/2412.07818/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios](https://mlsanity.com/paper/2004.05835/) (2020)
- [ResNet or DenseNet? Introducing Dense Shortcuts to ResNet](https://mlsanity.com/paper/2010.12496/) (2020)
- [AttCDCNet: Attention-enhanced Chest Disease Classification using X-Ray Images](https://mlsanity.com/paper/2410.15437/) (2024)

## Abstract (arXiv)

Internet of Things (IoT)-based healthcare systems offer significant potential for improving healthcare delivery in humanitarian and resource-constrained environments, providing essential services to underserved populations in remote areas. However, limited network infrastructure in such regions makes reliable communication challenging for traditional IoT systems. This paper presents a real-time chest X-ray decision support system designed for hospitals in remote locations. The proposed system integrates a fine-tuned ResNet50 deep learning model for disease classification with Fast DDS real-time middleware to ensure reliable and low-latency communication between healthcare practitioners and the inference system. Experimental results show that the model achieves an accuracy of 88.61%, precision of 88.76%, and recall of 88.49%. The system attains an average throughput of 3.2 KB/s and an average latency of 65 ms, demonstrating its suitability for deployment in bandwidth-constrained environments. These results highlight the effectiveness of DDS-based middleware in enabling real-time medical decision support for remote healthcare applications.