Research on liver tumor detection algorithm based on Deep Convolutional Neural Network
Liver tumor detection is performed using deep convolutional neural networks.Initially,liver tumor CT ima-ges are preprocessed.Then,threshold segmentation is performed on the images based on feature pixel values,and the tumor regions are labeled.A DCNN model is then established using the labeled dataset for training.Subsequently,the trained model is utilized to predict and validate on an unlabeled validation dataset.Finally,the model's performance is evaluated on a test dataset to detect liver tumors based on the test results.By comparing the detection results of the Deep Convolutional Neural Network algorithm,the watershed algorithm and the connected domain algorithm,the experimental results show that the DCNN algorithm achieves the highest accuracy and the highest F1 score in tumor detection.This indicates that DCNN ex-hibits superior performance in liver tumor detection,accurately identifying tumors and reducing the possibility of misdiagno-sis and missed diagnosis.
Deep Convolutional Neural NetworkTumor detectionF1 score
万方数据
维普
