Automatic recognition of bladder tumours using deep learning technology and its clinical application
R Yang, Y Du, X Weng, Z Chen… - … International Journal of …, 2021 - Wiley Online Library
R Yang, Y Du, X Weng, Z Chen, S Wang, X Liu
The International Journal of Medical Robotics and Computer …, 2021•Wiley Online LibraryBackground Bladder cancer is a kind of tumors with a high recurrence rate. The
improvement of the cure rate and prognosis of bladder tumor depends on the accurate
recognition of bladder tumor under the cystoscope. Aims To verify that deep learning
technology can identify bladder cancer images. Materials and Methods In this study, 1200
cystoscopic cancer images from 224 patients with bladder cancer and 1150 cystoscopic
images from 221 patients with no bladder cancer were collected. Three convolutional neural …
improvement of the cure rate and prognosis of bladder tumor depends on the accurate
recognition of bladder tumor under the cystoscope. Aims To verify that deep learning
technology can identify bladder cancer images. Materials and Methods In this study, 1200
cystoscopic cancer images from 224 patients with bladder cancer and 1150 cystoscopic
images from 221 patients with no bladder cancer were collected. Three convolutional neural …
Background
Bladder cancer is a kind of tumors with a high recurrence rate. The improvement of the cure rate and prognosis of bladder tumor depends on the accurate recognition of bladder tumor under the cystoscope.
Aims
To verify that deep learning technology can identify bladder cancer images.
Materials and Methods
In this study, 1200 cystoscopic cancer images from 224 patients with bladder cancer and 1150 cystoscopic images from 221 patients with no bladder cancer were collected. Three convolutional neural networks (LeNet, AlexNet and GoogLeNet), and the EasyDL deep learning platform were used to train deep learning models to distinguish images of bladder cancer. The diagnostic efficiency of deep learning model and urology experts was compared.
Results
The efficiency of EasyDL was the highest, and the accuracy was 96.9%. The efficiency of GoogLeNet was the second highest, and the accuracy was 92.54%. Among the 33 bladder cancer nodes and 11 no bladder cancer nodes, the accuracy of the neural network was 83.36% and that of medical experts was 84.09% (p > 0.05).
Discussion
This study used convolutional neural networks to recognize bladder tumor in the clinical. Although these three networks (LeNet, AlexNet and GoogLeNet) had a relatively basic network architecture, they achieved good results in the classification task of cystoscopic images. The deep learning system had a recognition efficiency no less than that of experienced clinical experts.
Conclusion
This study proved the validity of the convolutional neural network for bladder tumor diagnosis based on the cystoscope.
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