Design of pathological image classifier for lung cancer based on deep learning
Pathological examination is the"gold standard"for doctors to determine whether a tumor has undergone cancerous transformation.However,due to the multiple subtypes of lung cancer pathological tissue,doctors need to repeatedly review a large number of films in order to finally give a medical diagnosis,which is not only time-consuming but also prone to errors.Therefore,this article utilizes deep learning to study the subtype classification of lung cancer pathological tissues.By preprocessing the database data,finding the feature values,and using different layers of ResNet algorithms to construct lung cancer pathology image classification models,after adjusting the model parameters to the optimal value,we compared the training results of three different network models:ResNet18,ResNet34,and ResNet50.We analyzed the accuracy,recall,F1-score,and arithmetic mean of the models,and found that the ResNet34 model had the best performance in terms of classification accuracy for lung cancer pathology images.
Lung cancer subtypesDeep learningResNet modelPathological imagesAdamax optimizer