Research And Development Of Oral Disease Intelligent Diagnosis System Based On Deep Learning

Oral diseases such as dental calculus,dental caries and gingivitis have a high prevalence in China.The development of oral disease intelligent diagnosis system plays an important role in the early detection and timely treatment of oral diseases,and is of great significance to improve the oral health level of the whole nation.Labelme labeling software was used to label 7220 oral images.The images were labeled by drawing polygons and given different semantic categories to different polygons.After that,the data set was preprocessed,and the original images and annotation information files were processed into 8-bit depth maps through scripts in batches for model training.Using the 9:1 training strategy,the training data set accounted for 90%(6498 pieces),and the test data set accounted for 10%(722 pieces).Then,the identification and diagnosis of oral diseases need to locate and classify the lesions,and semantic segmentation can be used to identify the lesions.The network structure and basic principle of various semantic segmentation models in recent years are studied,and three semantic segmentation models based on encoder-decoder principle are selected as theoretical guidance.Therefore,based on U-NET,PSPNet and DeepLabV3+,three types of oral disease lesion recognition models were constructed.After 50 generations of training,the loss function converges.The results show that compared with the other two models,the recognition effect of U-NET model is more accurate,and mlou and mPA evaluation indexes are higher.Secondly,the improvement methods of convolutional neural network such as attention mechanism are studied in depth.The principles and functions of different attention mechanisms are summarized and the model is reconstructed.Based on the common channel and spatial attention mechanism,three kinds of attention modules,SENet,CBAM and ECANet,are used.Three models of focus segmentation of oral diseases based on DR-UNet,CS-UNET and FD-UNET were established.The three networks used the same training data set for training.After 80 iterations of epoch,the loss function converges,and the loss function of the improved network model has decreased to some extent and improved to some extent under mlou and mPA,among which CS-UNET has the best effect.Based on the above research,a B/S architecture oral disease diagnosis system was designed and implemented.The system has functions of login and logout,diagnosis and user management based on image recognition.Administrators can manage the system and common users,and common users can use the diagnostic function of the platform.The deep learning model is integrated into the background to perform image recognition,and the background database will record the diagnosis history.