A Real-Time Laryngeal Disease Diagnosis Algorithm on Edge-AI

Background: Laryngeal lesions represent a significant clinical challenge due to the complexity of the laryngeal structure, making manual diagnosis time-consuming and prone to subjective errors. Therefore, developing an accurate and lightweight automatic detection method is essential for improving the efficiency of laryngeal disease screening and diagnosis. Methods: This study proposes MSBA-YOLO, a lightweight laryngeal disease detection algorithm based on an improved YOLOv5s architecture. The method integrates FasterNet as the backbone network to reduce computational redundancy through partial convolutions and incorporates a Single-Head Self-Attention mechanism to capture long-range dependencies in complex lesion features. In addition, an MSBA-FIoU loss function is introduced to enhance the localization accuracy of multi-scale targets. Results: Experimental results show that MSBA-YOLO achieves a mean Average Precision (mAP) of 96.1% with a model size of only 6.4 MB, representing a 54.6% reduction in parameters compared with the baseline model. When deployed on the Jetson Orin Nano edge platform, the proposed method achieves real-time inference with a speed exceeding 50 FPS while maintaining low power consumption of 5.82 W. Conclusions: The results demonstrate that MSBA-YOLO effectively balances detection accuracy and computational efficiency, providing a robust and practical solution for portable and real-time clinical screening of laryngeal diseases on edge devices.