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EfficientNet-B3-Based Automated Deep Learning Framework for Multiclass Endoscopic Bladder Tissue Classification.

A A Abd El-Aziz1, Mahmood A Mahmood1, Sameh Abd El-Ghany1

  • 1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|October 16, 2025
PubMed
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This study introduces an automated deep learning system for bladder cancer detection. The EfficientNet-B3 model accurately classifies smooth muscle images, aiding early diagnosis and reducing healthcare costs.

Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Bladder cancer (BLCA) diagnosis is challenging due to tumor heterogeneity and complex histopathology.
  • Manual classification of tissues is time-consuming, prone to errors, and lacks standardization.
  • There is a critical need for automated, reliable systems for efficient BLCA detection.

Purpose of the Study:

  • To develop and validate a deep learning model for automated early detection of bladder cancer.
  • To improve the accuracy and efficiency of BLCA diagnosis compared to traditional methods.
  • To reduce diagnostic time and costs for patients undergoing bladder cancer screening.

Main Methods:

  • A deep learning approach using the EfficientNet-B3 model was employed for multiclass classification.
Keywords:
EfficientNet-B3Endoscopic Bladder Tissue Classification datasetbladder cancerdeep learningendoscopic images

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  • The Endoscopic Bladder Tissue Classification (EBTC) dataset was utilized, with preprocessing including resizing and normalization.
  • Five-fold cross-validation and ablation studies were performed to optimize hyperparameters and validate performance against other leading DL models.

    • Main Results:

    • The EfficientNet-B3 model achieved high performance metrics: 99.03% accuracy, 99.30% specificity, 97.95% precision, 96.85% recall, and 97.36% F1-score.
    • The proposed model significantly outperformed five other leading deep learning models in classifying bladder tissue images.
    • The system demonstrated efficient and accurate identification of bladder cancer tissues.

    Conclusions:

    • The EfficientNet-B3 model shows significant potential as a valuable tool for accurate and efficient bladder cancer diagnosis.
    • Automated classification using this DL model can streamline the diagnostic process, leading to timely interventions.
    • This technology offers a promising solution to reduce the morbidity and mortality associated with bladder cancer.