Abstract：
Facial Expression Analysis (FEA) plays a vital role in diagnosing and treating early-stage neurological disorders (NDs) like Alzheimer\'s and Parkinson\'s. Manual FEA is hindered by expertise, time, and training requirements, while automatic methods confront difficulties with real patient data unavailability, high computations, and irrelevant feature extraction. To address these challenges, this paper proposes a novel approach: an efficient, lightweight convolutional block attention module (CBAM) based deep learning network (DLN) to aid doctors in diagnosing ND patients. The method comprises two stages: data collection of real ND patients, and pre-processing, involving face detection and an attention-enhanced DLN for feature extraction and refinement. Extensive experiments with validation on real patient data showcase compelling performance, achieving an accuracy of up to 73.2%. Despite its efficacy, the proposed model is lightweight, occupying only 3MB, making it suitable for deployment on resource-constrained mobile healthcare devices. Moreover, the method exhibits significant advancements over existing FEA approaches, holding tremendous promise in effectively diagnosing and treating ND patients. By accurately recognizing emotions and extracting relevant features, this approach empowers medical professionals in early ND detection and management, overcoming the challenges of manual analysis and heavy models. In conclusion, this research presents a significant leap in FEA, promising to enhance ND diagnosis and care.The code and data used in this work are available at: https://github.com/munsif200/Neurological-Health-Care.
摘要：
面部表达分析（FEA）在诊断和治疗早期神经系统疾病（ND）如阿尔茨海默氏症和帕金森氏症中起着至关重要的作用。手动FEA受到专业知识的阻碍，时间,和培训要求，虽然自动方法面临着实际患者数据不可用的困难，高计算量，和不相关的特征提取。为了应对这些挑战，本文提出了一种新的方法：一种有效的，基于深度学习网络（DLN）的轻量级卷积块注意模块（CBAM），以帮助医生诊断ND患者。该方法包括两个阶段：真实ND患者的数据收集，和预处理，涉及人脸检测和用于特征提取和细化的注意力增强DLN。对真实患者数据进行验证的广泛实验展示了引人注目的性能，达到高达73.2%的精度。尽管它的功效,所提出的模型是轻量级的，只占用3MB，使其适合部署在资源受限的移动医疗设备上。此外，该方法比现有的有限元分析方法有了显著的进步，在有效诊断和治疗ND患者方面有着巨大的希望。通过准确识别情绪并提取相关特征，这种方法使医疗专业人员能够进行早期ND检测和管理，克服人工分析和重型模型的挑战。总之,这项研究提出了FEA的重大飞跃，承诺加强ND诊断和护理。这项工作中使用的代码和数据可在以下网址获得：https://github.com/munsif200/Neurological-Health-Care。
