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These features were used in support vector machine (SVM) classifiers to predict the grade and stage. Our proposed method segmented the tumor area and predicted the grade and stage more accurately compared to different methods in our experiments on MRI images. The accuracy of bladder tumor grade prediction was about 70%, and the accuracy of the data set was about 77.5%. The extensive experiments demonstrated the usefulness and effectiveness of our method.<\/jats:p>","DOI":"10.3233\/jifs-210263","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T22:23:34Z","timestamp":1618352614000},"page":"12139-12150","source":"Crossref","is-referenced-by-count":1,"title":["An optimized automatic prediction of stage and grade in bladder cancer based on U-ResNet"],"prefix":"10.1177","volume":"40","author":[{"given":"Xin-Zi","family":"Cao","sequence":"first","affiliation":[{"name":"School of Software, South China Normal University, Guangzhou, China"}]},{"given":"Sheng-Zhou","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Software, South China Normal University, Guangzhou, China"}]},{"given":"Jing-Cong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software, South China Normal University, Guangzhou, 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