Ultra-Low Latency Hybrid Feature Learning for Accurate Multiple Sclerosis Detection Using Multi-Scale Extraction and Adaptive Attention
DOI:
https://doi.org/0.54228/mjaret0624028Keywords:
Multiple Sclerosis (MS); Ultra-Low Latency; Feature Learning; Hybrid Multi-Scale Feature Extraction; Adaptive Attention; Medical Imaging; Real-Time Detection; Machine Learning; Neural NetworksAbstract
Multiple Sclerosis (MS) is a severe neurological disease and requires a fast and precise diagnosis for the optimal treatment outcome. This paper proposes a novel model that combines the hybrid multi-scale feature extraction with an adaptive attention mechanism to achieve ultra-low latency feature learning for MS detection in medical imaging. The proposed deep learning models have significantly improved the detection accuracy and reduced the latency. It reached 95% detection accuracy within 50 ms latency which means a 15% increase over the conventional ones. The adaptive attention mechanisms pay particular attention to key features which further increases the classification accuracy. This advanced detection model can be used in real-time monitoring and interventions which can effectively improve Multiple Sclerosis management.
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