An optimized automatic prediction of stage and grade in bladder cancer based on U-ResNet
- Resource Type
- Authors
- Xin-Zi Cao; Sheng-Zhou Luo; Jing-Cong Li; Jia-Hui Pan
- Source
- Journal of Intelligent & Fuzzy Systems. 40:12139-12150
- Subject
- Statistics and Probability
Oncology
medicine.medical_specialty
Bladder cancer
business.industry
General Engineering
02 engineering and technology
medicine.disease
Residual neural network
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Artificial Intelligence
Internal medicine
0202 electrical engineering, electronic engineering, information engineering
Medicine
020201 artificial intelligence & image processing
Stage (cooking)
business
- Language
- ISSN
- 1875-8967
1064-1246
The grade and stage of bladder tumors is an essential key for diagnosing and treating bladder cancer. This study proposed an automated bladder tumor prediction system to automatically assess the bladder tumor grade and stage automatically on Magnetic Resonance Imaging (MRI) images. The system included three modules: tumor segmentation, feature extraction and prediction. We proposed a U-ResNet network that automatically extracts morphological and texture features for detecting tumor regions. 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.