A symptom, blood data and medical image fused method for comprehensively recognizing various suspected diseases, the method using an artificial intelligence theory and intelligent recognition and intelligent analysis techniques. Symptoms, blood data and medical images are fused, and multi-data association is used for determining the relationship between data and disease recognition and a recognition rate. A disease feature item dynamically input by using a simulated annealing method is used as an input layer, an outer loop is set as a disease feature input item, an inner-layer loop is set as a feature corresponding to diseases and is used as an input item, and an improved deep neural network method is used for adjusting parameters and intelligently recognizing diseases. The dynamic input to an improved neural network framework is achieved, parameters are adaptively adjusted for disease recognition by means of interlayer calculations, and the efficiency of intelligent disease recognition is improved. By means of an artificial intelligence method, the problems of difficult multi-disease recognition, missing of other suspected diseases, etc. are solved, and comprehensive recognition of various suspected diseases becomes a key and is now used in specialized hospital departments and physical examination medical centers.
SYMPTOM, BLOOD DATA AND MEDICAL IMAGE FUSED METHOD FOR COMPREHENSIVELY RECOGNIZING VARIOUS SUSPECTED DISEASES