The application relates to the technical field of
oral cavity diagnosis, in particular to an
oral cavity diagnosis method for establishing an
oral cavity diagnosis neural
network model, which comprises the following steps: collecting oral cavity CT,
endoscope hard tissue three-dimensional coordinates,
soft tissue color parameters,
tooth surface texture gray value parameters, checking and removing abnormal values to generate an oral cavity standardized
original data set, extracting features and assigning weights to generate a weighted
signal set, iteratively training to obtain diagnosis network weight parameters, calculating
lesion matching degrees, dividing grades to establish oral cavity
lesion quantitative grading data, in the application, oral cavity
hard tissue three-dimensional coordinates,
soft tissue color parameters and
tooth surface texture gray value parameters are collected, values exceeding the normal physiological range are checked and removed to generate a standardized
data set, features are extracted, converted into discrete signals and assigned with degree of distinction weights, stable diagnosis criteria are established through hierarchical transmission calculation and iterative training, accurate positioning and quantitative grading of oral cavity lesions are realized, and the diagnosis precision, efficiency and result consistency are greatly improved.