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Microscopic urinary particle detection by different YOLOv5 models with evolutionary genetic algorithm based

K Suhail¹, D Brindha¹

¹Department of Biomedical Engineering, PSG College of Technology, Coimbatore, 641004, India.

Computers in Biology and Medicine

|January 6, 2024

Summary

This study introduces an advanced YOLOv5 deep learning model for automated urinalysis, significantly improving kidney disease diagnosis by accurately detecting urine sediment particles with high precision and speed.

Keywords:

Deep learning Evolutionary genetic algorithm Hyperparameter optimization Urinalysis YOLOv5

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Area of Science:

Medical Imaging
Artificial Intelligence
Nephrology

Background:

Traditional urinalysis is manual, labor-intensive, and prone to errors.
Automated microscopy requires complex segmentation and feature extraction, hindering machine learning model performance.
Deep learning models, like CNNs, often need extensive annotated data, increasing computational complexity.

Purpose of the Study:

To develop an advanced, faster deep learning model for automated urine sediment analysis.
To enhance the accuracy and efficiency of kidney disease diagnosis through improved urinalysis.
To address the limitations of existing automated microscopy and deep learning approaches.

Main Methods:

Implemented five variants of the YOLOv5 deep learning model (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x).
Utilized a dataset of 5376 microscopic urine sediment images covering six particle categories.
Employed an Evolutionary Genetic Algorithm (EGA) to optimize hyperparameters for model training.

Main Results:

YOLOv5l and YOLOv5x models demonstrated superior performance, achieving mean average precisions (mAP) of 85.8% and 85.4% respectively.
Mycete particles showed the highest detection performance (97.6% mAP with YOLOv5x), while crystals had the lowest (81.7% mAP).
Achieved fast detection speeds of 23.4 ms/image (YOLOv5l) and 28.4 ms/image (YOLOv5x).

Conclusions:

The proposed YOLOv5-based deep learning model offers a faster and more accurate automated solution for urinalysis.
This advanced model effectively detects urinary particles from microscopic images, aiding in kidney disease identification.
The study highlights the potential of deep learning in improving the efficiency and reliability of diagnostic processes.