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

Rui Yang1, Yang Du1, Xiaodong Weng1

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

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|October 29, 2020
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Summary

Deep learning models accurately identify bladder cancer from cystoscopic images, matching expert performance. This technology aids in early diagnosis and improves patient outcomes for this common cancer.

Keywords:
bladder cancerconvolutional neural networkcystoscopedeep learning

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Area of Science:

  • Artificial Intelligence in Medicine
  • Oncology
  • Medical Imaging Analysis

Background:

  • Bladder cancer has a high recurrence rate, necessitating accurate cystoscopic identification for improved prognosis.
  • Early and precise diagnosis of bladder tumors is crucial for effective treatment and patient outcomes.

Purpose of the Study:

  • To evaluate the efficacy of deep learning technology in identifying bladder cancer from cystoscopic images.
  • To compare the diagnostic accuracy of deep learning models against that of urology experts.

Main Methods:

  • Trained three convolutional neural networks (LeNet, AlexNet, GoogLeNet) and the EasyDL platform on 1200 bladder cancer and 1150 non-cancer cystoscopic images.
  • Collected data from 224 patients with bladder cancer and 221 without.
  • Compared the diagnostic performance of the developed deep learning models with that of experienced urology experts.

Main Results:

  • The EasyDL platform achieved the highest accuracy at 96.9%, followed by GoogLeNet at 92.54%.
  • Deep learning models demonstrated a diagnostic accuracy of 83.36%, comparable to urology experts at 84.09% (p > 0.05).
  • The study confirmed the effectiveness of convolutional neural networks in classifying cystoscopic images.

Conclusions:

  • Deep learning, specifically convolutional neural networks, is a valid tool for bladder tumor diagnosis using cystoscopic images.
  • The diagnostic efficiency of deep learning systems is comparable to that of experienced clinical experts.
  • This technology holds promise for improving the accuracy and efficiency of bladder cancer detection.