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Hybrid Neural Networks for Precise Hydronephrosis Classification Using Deep Learning.

Abdus Salam1, Mansura Naznine2, Muhammad E H Chowdhury3

  • 1Department of Electrical and Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh.

Urology
|August 9, 2025
PubMed
Summary

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This summary is machine-generated.

This study introduces a deep learning framework for automated kidney and fluid segmentation in renal ultrasound images. The system achieves high accuracy in segmenting structures and classifying hydronephrosis, improving diagnostic consistency.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Urology

Background:

  • Hydronephrosis assessment using renal ultrasound images can be subjective and variable.
  • Accurate segmentation of kidney and fluid is crucial for reliable hydronephrosis diagnosis.

Purpose of the Study:

  • To develop and evaluate a deep learning framework for automatic kidney and fluid segmentation in renal ultrasound images.
  • To enhance diagnostic accuracy and reduce variability in hydronephrosis assessment.

Main Methods:

  • A dataset of 1731 renal ultrasound images was used for training and evaluation.
  • A deep learning framework with DenseNet201, Feature Pyramid Network (FPN), and Self-Organizing Neural Network (SelfONN) layers was implemented.
  • Segmentation performance was evaluated using Dice coefficient, precision, and recall; hydronephrosis classification used the fluid-to-kidney area ratio.

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Main Results:

  • The framework achieved high segmentation performance for kidneys (Dice: 0.92) and fluid (Dice: 0.89).
  • Hydronephrosis classification accuracy reached 94% using the fluid-to-kidney area ratio.
  • The model demonstrated robust performance across varied image qualities.

Conclusions:

  • The developed framework provides an automated and objective pipeline for renal ultrasound image analysis.
  • The system facilitates standardized and reproducible hydronephrosis assessment with high accuracy.
  • Future work includes model optimization and integration of explainable AI for clinical use.