Abstract
Advances in mixed reality and blockchain technology in Metaverse are transforming the digital healthcare by enabling real-time health monitoring and remote medical treatments. This significantly enhances patient care and medical interactions, permitting virtual consultations without the need for physical hospital visits. This study utilized a dynamic vision sensor camera to track white blood cells and employed spiking neural networks to classify and analyze event-based data from nail capillaries. Additionally, the soft-reset integrate-and-fire neuron model, which adjusts the membrane potential by subtracting the threshold post-spike, was used to optimize the training of spiking neural networks. Surrogate gradient learning, which approximates gradients for non-differentiable spiking functions, was also applied to enhance the training process. The results confirmed that the classification performance of white blood cells achieved an accuracy of 80.48%, with precision and recall at 99.65% and 78.47% respectively, and F1-score of 86.62%.
Youngshin Kang
PhD Student
Her research interests include lightweight deep learning algorithms, signal processing, and the Internet of Things.
Geunbo Yang
Researcher, KG Steel
His research interests include machine learning algorithms and spiking neural networks.
Cheolsoo Park
Professor
His research interests include machine learning, adaptive signal processing, computational neuroscience, and wearable technology.