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Related Experiment Video

Updated: Dec 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K

A deep learning network-assisted bladder tumour recognition under cystoscopy based on Caffe deep learning framework

Yang Du1, Rui Yang1, Zhiyuan Chen1

  • 1Department of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS
|September 18, 2020
PubMed
Summary

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Automatic recognition of bladder tumours using deep learning technology and its clinical application.

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Nihon Geka Gakkai zasshi·2007

Deep learning accurately identifies bladder cancer from cystoscopy images. A mobile-deployable model achieved 96.9% accuracy, aiding clinical diagnosis.

Area of Science:

  • Urology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Cystoscopy is crucial for diagnosing bladder tumors.
  • Deep learning, specifically convolutional neural networks, excels in image recognition and segmentation.

Purpose of the Study:

  • To evaluate the accuracy of a deep learning model for bladder cancer detection using cystoscopy images.
  • To assess the feasibility of deploying this model on mobile devices for clinical application.

Main Methods:

  • Trained a deep learning model using 1,200 normal bladder images and 734 bladder tumor images from 175 patients.
  • Utilized the Caffe deep learning framework and the EasyDL platform for model development.
  • Deployed the trained model on a mobile phone for real-time analysis.
Keywords:
Caffe frameworkEasyDL frameworkbladder cancerconvolution neural networkdeployrecognition

Related Experiment Videos

Last Updated: Dec 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K

Main Results:

  • The deep learning model achieved an 82.9% accuracy rate using the Caffe framework.
  • The model trained on the EasyDL platform demonstrated a 96.9% accuracy rate.
  • The EasyDL-based model accurately identified bladder cancer on mobile and web platforms.

Conclusions:

  • Deep learning networks can accurately recognize bladder cancer from cystoscopic images.
  • Mobile deployment of deep learning models offers a practical tool for clinical use in bladder cancer diagnosis.