Pediatric chest X-ray diagnosis using neuromorphic models.
Authors
Affiliations (3)
Affiliations (3)
- Department of Electrical and Computer Engineering, University of Engineering and Technology, Taxila, Pakistan.
- Department of Electrical and Electronic Engineering, University of Jeddah, Saudi Arabia.
- School of Computing, Ulster University, Belfast, BT15 1ED, UK. Electronic address: [email protected].
Abstract
This research presents an innovative neuromorphic method utilizing Spiking Neural Networks (SNNs) to analyze pediatric chest X-rays (PediCXR) to identify prevalent thoracic illnesses. We incorporate spiking-based machine learning models such as Spiking Convolutional Neural Networks (SCNN), Spiking Residual Networks (S-ResNet), and Hierarchical Spiking Neural Networks (HSNN), for pediatric chest radiographic analysis utilizing the publically available benchmark PediCXR dataset. These models employ spatiotemporal feature extraction, residual connections, and event-driven processing to improve diagnostic precision. The HSNN model surpasses benchmark approaches from the literature, with a classification accuracy of 96% across six thoracic illness categories, with an F1-score of 0.95 and a specificity of 1.0 in pneumonia detection. Our research demonstrates that neuromorphic computing is a feasible and biologically inspired approach to real-time medical imaging diagnostics, significantly improving performance.