A deep learning network‐assisted bladder tumour recognition under cystoscopy based on Caffe deep learning framework and EasyDL platform. Issue 1 (24th September 2020)

• Record Type: Journal Article
• Authors: Du, Yang; Yang, Rui; Chen, Zhiyuan; Wang, Lei; Weng, Xiaodong; Liu, Xiuheng
• Is Part Of: International journal of medical robotics and computer assisted surgery. Volume 17:Issue 1(2021)
• Page Start: 1
• Page End: 8
• Publication Date: 2020-09-24
• Subjects: bladder cancer -- Caffe framework -- convolution neural network -- deploy -- EasyDL framework -- recognition; Robotics in medicine -- Periodicals; Surgery -- Technological innovations -- Periodicals; Imaging systems in medicine -- Periodicals; 617.90285
• DOI: 10.1002/rcs.2169
• Languages: English
• ISSNs: 1478-5951

Abstract

Abstract: Background: Cystoscopy plays an important role in the diagnosis of bladder tumours. 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. Methods: One thousand two photographs of normal bladder tissue and 734 photos of bladder tumours 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. Results: 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 were 96.9%. The model from EasyDL platform could discern bladder cancer accurately on the phone and website. Conclusion: The deep learning network could recognise the bladder cancer accurately. Deploying that model on the mobile phone was useful for clinical use.