Pediatric chest X-ray diagnosis using neuromorphic models

• Published in: Computers in biology and medicine Vol. 192; no. Pt A; p. 110173
• Main Authors: Bokhari, Syed Mohsin, Sohaib, Sarmad, Shafi, Muhammad
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2025
• Subjects: Child; Child, Preschool; Female; Hierarchical Spiking Neural Networks (HSNN); Humans; Infant; Internal Medicine; Machine Learning; Male; Neural Networks, Computer; Neuromorphic computing; Other; Pediatric chest X-rays (pediCXR); Radiographic Image Interpretation, Computer-Assisted - methods; Radiography, Thoracic - methods; Spiking convolutional neural networks (SCNN); Spiking Neural Networks (SNNs); Spiking Neural Networks (SNNs); Pediatric chest X-rays (pediCXR); Hierarchical Spiking Neural Networks (HSNN); Spiking convolutional neural networks (SCNN); Neuromorphic computing

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 R...