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

TL;DR

This paper introduces a real-time chest X-ray decision support system for remote clinics, combining deep learning and DDS middleware to ensure reliable, low-latency communication in bandwidth-limited environments.

Contribution

It presents a novel IoT-based healthcare system integrating a fine-tuned ResNet50 model with DDS middleware for effective remote medical diagnosis.

Findings

Achieved 88.61% accuracy in disease classification

System maintains low latency of 65 ms in bandwidth-constrained settings

Demonstrated reliable real-time communication in resource-limited environments

Abstract

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.