Research on the construction of image detection system for early gastric cancer by artificial intelligence YOLO-V8 algorithm
JIN Tao
YAO Zhendong
MAO Boneng
JIANG Jianzhong
CHEN Yanchun
Abstract：Objective To verify the generalization performance of YOLO-V8 on the endoscopic images of gastric cancer,to evaluate its accuracy and stability,improve the YOLO-V8 system,optimize the EGC-YOLO-V8 AI system,and improve the parameters configuration.Methods Gastroscopic image cases from Yixing People's Hospital from 2020 to 2022 were collected and divided into two categories according to their pathological types,including early gastric cancer images(positive samples)and non-gastric cancer images(negative samples);Divide the datasets into training and test sets.Establish the training sets,adjust the parameters of the model,select the model by using the test sets,test and analyze,set the threshold,record the sensitivity,specificity,precision,Youden index,using the Receiver Operating Characteristic(ROC)Curve assessment method,calculate the Area Under the Curve(AUC),to set the parameters for the best generalization effect.Results The average AUC of training set 1 is 0.769,corresponding to the maximum Youden index 0.614,sensitivity 0.723,specificity 0.891,precision 0.844,corresponding threshold 0.01.The average AUC of training set 2 is 0.804,corresponding to the the maximum Youden index 0.669,sensitivity 0.688,specificity 0.981,precision 0.882,corresponding threshold 0.02.Conclusion In this study,the best threshold is set at 0.02,and the ratio of positive and negative samples in the best training set is 1:2,the system could reflect better generalization performance.
Keywords：Early gastric cancerArtificial intelligenceYOLO-V8Endoscopic diagnosis
Publication Date：2025-03-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 261-267 )