Research on Chronic Disease Auxiliary Diagnosis System Based on Multimodal Deep Learning

This research examines a system for assisting in the diagnosis of chronic diseases using multi-modal deep learning, with the goal of enhancing the precision and speed of chronic disease diagnosis by combining various types of information like images, text, and clinical data. The research developed a hierarchical system design that consisted of layers for data collection, feature extraction, data fusion, and decision-making. Every level has the responsibility of handling and combining data from various sources in multiple modes. By utilizing feature-level fusion technology, feature details from various modalities are merged to provide a thorough grasp and diagnosis of the illness. Experimental findings reveal that the diagnostic capabilities of the suggested system experience a notable enhancement following the fusion of multi-modal data. In comparison to single-modal approaches, both accuracy and resilience see significant improvement. Case studies confirm the system's efficiency on actual data sets, showcasing its promise in early detection of chronic illnesses and crafting tailored treatment strategies. The findings of this study offer a fresh approach to effectively managing chronic illnesses, with significant practical and promotional implications. This system can more effectively address the requirements of diagnosing complex diseases and offer technical assistance for the smart advancement of the medical field.