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Related Experiment Video

Updated: Jan 31, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
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Ultrasound-Based Kidney Stone Classification Using Kronecker Self-Organizing Map Forward Harmonic Network.

Pendela Kanchanamala1, Kishore Bhamidipati2, Rohini Arunachalam3

  • 1Department of CSE-AI & DS, GMR Institute of Technology, Rajam, Andhra Pradesh, India.

Ultrasonic Imaging
|January 30, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning model, Kronecker Self-Organizing Map Forward Harmonic Network (KSOMFHNet), accurately classifies kidney stones from ultrasound images. This automated approach improves diagnostic efficiency and accuracy in urological disorder detection.

Keywords:
deep Kronecker networkdeep-recursive residual networkkidney stonekidney stone classificationself-organizing map network

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Area of Science:

  • Urology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Kidney stone disease is a common urological condition with potential for severe complications.
  • Traditional ultrasound diagnosis of kidney stones relies on manual interpretation, facing limitations in accuracy and efficiency.
  • Automated diagnostic tools are needed to overcome subjective interpretation and enhance timely detection.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model for automated kidney stone classification using ultrasound imagery.
  • To improve diagnostic accuracy and efficiency compared to traditional methods.

Main Methods:

  • A deep learning model, Kronecker Self-Organizing Map Forward Harmonic Network (KSOMFHNet), was developed.
  • Image preprocessing involved double bilateral filtering for denoising.
  • Kidney segmentation was performed using Deep Recursive Residual Network (DRRN).
  • Feature extraction combined BRIEF, shape, and GLCM descriptors.
  • Classification utilized a hybrid DKN and SOMNet architecture.

Main Results:

  • KSOMFHNet achieved high performance metrics on training data.
  • Accuracy: 91.984%
  • True Positive Rate (TPR): 90.543%
  • True Negative Rate (TNR): 92.248%
  • Precision: 90.179%
  • F1-score: 90.360%

Conclusions:

  • The proposed KSOMFHNet demonstrates significant potential for accurate and efficient kidney stone classification.
  • The automated deep learning approach offers a promising alternative to manual interpretation in clinical settings.
  • Further validation is recommended for widespread clinical deployment.