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Related Experiment Video

Updated: Sep 15, 2025

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Optimised RFO tuned RF-DETR model for precision urine microscopy for renal and systemic disease diagnosis.

Neeraj Dahiya1, Deo Prakash2, Shakti Kundu3

  • 1Department of Computer Science & Engineering, SRM University, Delhi-NCR, Sonipat, Haryana, India.

Scientific Reports
|July 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized RF-DETR model using Red Fox Optimization for automated urine analysis. The AI model accurately detects urinary components, aiding in early disease diagnosis with high precision and low latency.

Keywords:
Automated diagnosisHyperparameter tuningObject detectionRF-DETR modelRed Fox optimization (RFO)Urine microscopy

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Accurate urine microscopy analysis is crucial for diagnosing renal and systemic diseases.
  • Automating urine sediment analysis can improve diagnostic efficiency and accuracy.
  • Existing methods may lack precision or speed for real-time clinical applications.

Purpose of the Study:

  • To develop and optimize an object detection framework for automated urine sediment analysis.
  • To enhance the precision and reduce latency in classifying cellular and non-cellular urine components.
  • To validate the performance of the proposed model on a relevant dataset.

Main Methods:

  • Implementation of a transformer-based object detection model (RF-DETR) with deformable attention and a DINOv2 backbone.
  • Optimization of hyperparameters using the Red Fox Optimization (RFO) metaheuristic algorithm.
  • Training and evaluation on the RF100-VL urine microscopy dataset.

Main Results:

  • The RFO-optimized RF-DETR achieved a precision of 0.78 and mAP@0.5 of 0.737.
  • Demonstrated improved detection of small objects like leukocytes and erythrocytes compared to baseline models.
  • Enabled real-time inference suitable for GPU and edge devices due to NMS-free design and multi-resolution training.

Conclusions:

  • The RFO-optimized RF-DETR framework is a robust and scalable tool for automated urine analysis.
  • The model shows potential for clinical deployment in early disease diagnosis.
  • Future work includes developing lightweight variants and exploring advanced learning techniques for improved performance.