MMPF: Multi-Modal Perception Framework for Abnormal Medical Condition Detection

Abstract

As the global population ages and the incidence of chronic diseases increases, the demand for early detection of abnormal medical conditions is increasing. Traditional health monitoring methods often require significant resources and specialized personnel, limiting their widespread use. Leveraging advancements in AI technologies, this study proposes a non-invasive method for detecting abnormal medical conditions from image data. A multimodal perception framework is introduced, integrating features from various modalities, including facial expressions and body postures, to enhance detection accuracy. The framework employs a Cascaded Squeeze-Excitation (CSE) module, consisting of Adaptive and Multi-modal Squeeze-Excitation components, to capture complex feature dependencies and improve cross-modal performance. Extensive experiments demonstrate the effectiveness of this approach, showing improved performance over existing methods. In addition, a new dataset that encompasses a wide range of medical conditions has been released, providing a valuable resource for future research in this domain.