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Related Experiment Video

Updated: May 13, 2025

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors
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Accurate bladder cancer diagnosis using ensemble deep leaning.

Rana A El-Atier1, M S Saraya2, Ahmed I Saleh2

  • 1Computers and Control Department, Faculty of Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt. eng.r.ahmed2288@gmail.com.

Scientific Reports
|April 15, 2025
PubMed
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A new Ensemble Deep Learning (EDL) model offers a more accurate and efficient method for diagnosing bladder cancer. This artificial intelligence approach combines multiple deep learning techniques to improve diagnostic accuracy and reduce errors in cancer detection.

Area of Science:

  • Urology
  • Oncology
  • Artificial Intelligence in Medicine

Background:

  • Bladder cancer diagnosis relies on invasive, costly, and time-consuming methods like biopsy.
  • Current biomarker tests for bladder cancer suffer from limited reliability due to high false positive and negative rates.
  • Artificial intelligence (AI) shows promise for improving urological disease diagnosis.

Purpose of the Study:

  • To introduce a novel Ensemble Deep Learning (EDL) model for accurate bladder cancer diagnosis.
  • To enhance diagnostic precision by integrating multiple AI algorithms.
  • To provide a more interpretable AI model for clinical decision-making.

Main Methods:

  • Data preprocessing involved outlier rejection using the interquartile range (IQR).
Keywords:
Bladder cancerDeep learningDiagnosisEnsemble classificationVoting

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  • The EDL model integrates three deep learning algorithms: Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), and Explainable Deep Learning (XDL) utilizing Guided Grad-CAM.
  • A novel majority voting mechanism integrates the outputs of CNN, GAN, and XDL for final diagnosis, considering accuracy in cases of divergent predictions.

    • Main Results:

    • The proposed EDL model demonstrated superior efficiency and accuracy in diagnosing bladder cancer compared to existing methods.
    • EDL achieved the highest accuracy rates and the lowest error rates.
    • The model also showed reduced execution time, indicating practical clinical utility.

    Conclusions:

    • The EDL model represents a significant advancement in AI-driven bladder cancer diagnosis.
    • This approach offers a more reliable, accurate, and potentially faster alternative to current diagnostic procedures.
    • The integration of explainable AI (XDL) enhances clinician trust and understanding of diagnostic outcomes.