基于改进3D卷积神经网络的疼痛检测

Pain Detection Based on Improved 3D Convolutional Neural Network

在临床实践中,精确评估疼痛对于疼痛管理和诊断至关重要.但传统的评估方法主观性高且依赖医生经验,迫切需要更可靠客观的替代方法.利用深度学习的方法实现基于面部表情的疼痛检测研究近年已取得显著进展,但复杂的结构和高计算成本制约了其实际应用.因此,本文提出了一个改进的3D卷积神经网络,采用轻量级的3D卷积神经网络L3D作为骨干网络,并结合改进的SE注意力机制,把多个不同尺度的特征进行融合,捕捉疼痛序列中具有较强辨别能力的时空特征.在UNBC-McMaster和BioVid数据集上进行评估,与最新方法相比,该方法在疼痛检测性能以及计算复杂度上取得了优势.

In clinical practice,accurate pain assessment is crucial for pain management and diagnosis.However,traditional assessment methods are highly subjective and reliant on the expertise of medical professionals,highlighting the urgent need for more reliable and objective alternatives.The research on pain detection based on facial expression by deep learning has made remarkable progress in recent years,whereas the complex structure and high computational cost restrict its practical application.Therefore,this study proposes an improved 3D convolutional neural network(CNN)that utilizes a lightweight 3D CNN named L3D as the backbone network.It also incorporates an enhanced SE attention mechanism to fuse multiple features of different scales,capturing spatiotemporal characteristics with strong discriminative power in pain sequences.The proposed method is evaluated on UNBC-McMaster and BioVid datasets.Compared with the state-of-the-art methods,the proposed method achieves superior performance in pain detection and computational complexity.