Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice
Objective To establish a assisted diagnosis model for mouse gastric squamous cell carcinoma,by implementing deep learning technology to improve the accuracy and consistency of pathological diagnosis.Methods A total of 93 cases of gastric squamous cell carcinoma tissue and 56 cases of normal mouse gastric tissue were collected form a carcinogenicity study.After scanning into digital slide images,semi-automated data annotation was performed.After preprocessing all data with tissues detection,artifact removal,and benign epithelial region removal,they were randomly divided into training set,validation set,and test set at a ratio of 8∶1∶1.Construct a DenseNet algorithm model based on the HALO AI platform to identify areas of gastric squamous cell carcinoma and non-squamous cell carcinoma.Evaluate the performance of the constructed algorithm model using precision,recall,and F1-score.Results The overall accuracy,recall and F1 score of the DenseNet algorithm model in the test set were 0.904,0.929 and 0.916,respectively.Conclusion The DenseNet algorithm model established in this study has good application prospects for assisting diagnosis of gastric squamous cell carcinoma in mouse.
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