Cystoscopy plays an important role in the diagnosis of bladder tumors. As a typical representative of the deep learning algorithm, the convolutional neural network has shown great advantages in the field of image recognition and segmentation.
1002 photographs of normal bladder tissue and 734 photos of bladder tumors under cystoscopy were taken from 175 patients. Caffe deep learning framework and EasyDL platform were used to structure and train the model. The trained model from the EasyDL platform was deployed on a mobile phone.
The accuracy rate of the neural network to recognise the bladder cancer based on Caffe framework was 82.9%, and the data on the EasyDL platform was 96.9%. The model came from EasyDL platform could discern bladder cancer accurately on the phone and website.
The deep learning network could recognise the bladder cancer accurately. Deploy that model on the mobile phone was useful for clinical use. This article is protected by copyright. All rights reserved.
The international journal of medical robotics + computer assisted surgery : MRCAS. 2020 Sep 18 [Epub ahead of print]
Yang Du, Rui Yang, Zhiyuan Chen, Lei Wang, Xiaodong Weng, Xiuheng Liu
Department of Urology, Renmin Hospital of Wuhan University, No.99, Zhang Zhi-dong Road, Wuhan, Hubei, 430060, P.R. China.