Research and Mobile Implementation of Multi modal Medical Auxiliary Diagnosis Framework Based on Sparse Bayesian Learning

Abstract

In recent years, a large amount of data resources has been accumulated in the fields of medical imaging, genetic information, and electronic medical records. Therefore, the effective integration of these different sources of data to improve disease diagnosis has received increasing attention. Therefore, this section proposes a sparse Bayesian learning method to achieve joint analysis between multiple medical data and apply it to assist doctors in judging patients' conditions. Due to its ability to simultaneously select variables and estimate their uncertainty, sparse Bayesian learning has good robustness. On the other hand, for clinical portable diagnostic applications, explore lightweight compression and mobile deployment methods for models, and develop real-time auxiliary diagnostic software on iOS/Android platforms. The experimental results show that the proposed framework performs well in various disease diagnosis tasks, and the efficiency of mobile operation meets real-time requirements, providing a new technological path for clinical auxiliary diagnosis.