Using AI to Detect Stroke Related Facial Weakness with Limited face images

Stroke is one of the leading causes of long-term disability and death worldwide, where rapid and accurate detection of early symptoms is critical for effective treatment and recovery. Among the primary indicators, facial weakness serves as a vital clinical cue in identifying stroke onset. Traditional diagnostic methods often rely on physical examination or advanced imaging, which may be time-consuming, resource-intensive, and inaccessible in remote or resource-limited areas. This project, Using AI to Detect Stroke Related Facial Weakness with Limited Face Images, proposes an artificial intelligence–driven approach to detect stroke symptoms from facial features using minimal image data. The system leverages deep learning techniques with optimized feature extraction and classification models to handle data scarcity while ensuring robust performance. By employing transfer learning with pre-trained convolutional neural networks and augmentation strategies, the framework enhances recognition accuracy under limited training conditions. The project flow includes data preprocessing, facial landmark detection, feature extraction, model training, evaluation, and deployment in a user-friendly interface for early stroke risk assessment. This AI-based system aims to provide a fast, non-invasive, and cost-effective screening tool that can support clinicians and empower telemedicine applications. Ultimately, it highlights the potential of machine learning in addressing critical healthcare challenges where medical expertise and diagnostic infrastructure are limited.

Keywords

Stroke Detection, Facial Weakness, Artificial Intelligence, Deep Learning, Limited Data, Transfer Learning, Healthcare AI, Convolutional Neural Networks, Medical Image Analysis, Early Diagnosis.