A review on various methods for recognition of urine particles using digital microscopic images of urine sediments

Suhail, Kausar; Brindha, D

doi:10.1016/j.bspc.2021.102806

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…Automated urine analysis relies on digital images of urinary sediments captured through a microscopic lens. The process involves three main steps: segmentation, feature extraction, and classification [10], [11]. Object segmentation is crucial for identifying and distinguishing individual cell types and sediments [12], [13].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

Akhtar,

Hanif,

Rashid

et al. 2024

IEEE Access

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Automated urine sediment analyzers play a crucial role in diagnosing urinary tract infections, offering real-time data analysis and expediting patient diagnosis. This paper introduces a novel hybrid approach combining data-centric and model-centric techniques for automated urine sediment analysis. The proposed methodology addresses challenges such as morphological similarities among particle classes, uneven particle distribution, and intra/inter-class variations. A modified version of convolutional neural network (CNN), specifically the Visual Geometry Group (VGG-19) model, incorporating transfer learning, along with data augmentation is proposed for automated urine sediment classification with 98% accuracy and impressive inference time of 61ms per image. The proposed approach outperforms existing methods, especially in handling diverse sediment categories, demonstrating its potential for practical applications in medical diagnostics. We proposed the integration of a data-centric approach for improved labeling reliability and a model-centric approach for fine-tuning of the deep learning model, showcasing promising results in recognizing 12 distinct urine sediment classes. This study also emphasizes the importance of collaboration with medical professionals in refining the model's performance and handling challenges related to data acquisition and class imbalance. The proposed approach provides a significant advancement in automating and enhancing urine sediment analysis processes.

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Section: Introductionmentioning

confidence: 99%

An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

Akhtar,

Hanif,

Rashid

et al. 2024

IEEE Access

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“…Every year, 830,000 people die worldwide due to kidney and urinary tract diseases [ 1 ]. It is a known fact that urine samples are taken from patients to be used in the diagnosis of many diseases, especially diabetes, metabolic, urinary, and kidney diseases.…”

Section: Introductionmentioning

confidence: 99%

“…Suhail et al presented a comprehensive review study to compare the performance of artificial intelligence techniques that classify microscope images of urine particles [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model

et al. 2023

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Urine sediment examination is one of the main tests used in the diagnosis of many diseases. Thanks to this test, many diseases can be detected in advance. Examining the results of this test is an intensive and time-consuming process. Therefore, it is very important to automatically interpret the urine sediment test results using computer-aided systems. In this study, a data set consisting of eight classes was used. The data set used in the study consists of 8509 particle images obtained by examining the particles in the urine sediment. A hybrid model based on textural and Convolutional Neural Networks (CNN) was developed to classify the images in the related data set. The features obtained using textural-based methods and the features obtained from CNN-based architectures were combined after optimizing using the Minimum Redundancy Maximum Relevance (mRMR) method. In this way, we aimed to extract different features of the same image. This increased the performance of the proposed model. The CNN-based ResNet50 architecture and textural-based Local Binary Pattern (LBP) method were used for feature extraction. Finally, the optimized and combined feature map was classified at different machine learning classifiers. In order to compare the performance of the model proposed in the study, results were also obtained from different CNN architectures. A high accuracy value of 96.0% was obtained in the proposed model.

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“…Yapay zeka sistemlerinin kullanıldığı güncel çalışmalarda idrarın mikroskobik analizleri için konvolüsyonel sinir ağları ya da kısaca CNN (Convolutional Neural Network) olarak ifade edilen derin öğrenme modelleri tercih edilmiştir (Suhail andBrindha 2021, Zeb et al 2020). Derin öğrenme sistemlerinin nesne tespitindeki başarısının klasik görüntü işleme yöntemlerine göre daha iyi olması tercih sebebi olmaktadır (Grenspan 2016).…”

Section: Introductionunclassified

Mask R-CNN ile Mikroskobik İdrar Görüntüsü İçeriklerinin Tespiti

YÖRÜK,

SARAOĞLU,

ÖZER

2023

Afyon Kocatepe University Journal of Sciences and Engineering

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Mikroskobik idrar içerikleri doğru ve dikkatli bir şekilde analiz edildiğinde vücut hakkında önemli bilgiler verir. İdrar tahlilinin insan sağlığı için önemi nedeniyle mikroskobik idrar içeriklerinin tespit edilmesi amacıyla derin öğrenme görüntü işleme tekniği kullanılarak yapay zeka uygulamaları yapılmıştır. Literatürde yer alan çalışmaların çoğunda genel olarak semantik segmentasyon üzerine yoğunlaşılmıştır. Bu çalışmada ise piksel düzeyinde segmentasyon yapabilen Mask R-CNN modeli ile mikroskobik idrar görüntülerindeki alyuvar, akyuvar, epitel, kristal, bakteri ve mantar içerikleri konum ve nesne türü bilgisiyle birlikte tespit edilmiştir. Mask R-CNN ile tespit edilen nesnelere maske ve çerçeve olmak üzere iki tip sınır çizilmektedir. Sistemin performansı her iki sınır tipi için ayrı ayrı incelenmiştir. Test için kullanılan 100 görüntüdeki toplam 1154 örüntüden maskelere göre 808 ve çerçevelere göre 843 nesne doğru şekilde tespit edilmiştir (IoU=0,5). En iyi tespit oranı akyuvarlar ve alyuvarlar için gerçekleşmiştir. Epiteller çerçevelere göre hesaplamada başarılı bir şekilde tespit edilmiştir fakat düzgün maske oluşturulamamıştır. Bakteriler diğerlerine göre çok küçük olduğu için doğru tespit oranı düşük kalmıştır. Kristallerin ve mantarların çoğu doğru şekilde tespit edilmiştir. Ayrıca, nesne tespitinde sıklıkla kullanılan değerlendirme metriği mAP de hesaplanmıştır. Sistem için hesaplanan mAP değerleri maskelere göre 0,7842 ve çerçevelere göre 0,8343 olmuştur. Mask R-CNN sistemi iyi bir şekilde optimize edilip daha fazla idrar içeriğine ait görüntülerle eğitilmesi durumunda idrar analiz sistemlerinde kullanılabilir.

show abstract

A review on various methods for recognition of urine particles using digital microscopic images of urine sediments

Cited by 10 publications

References 20 publications

An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model

Mask R-CNN ile Mikroskobik İdrar Görüntüsü İçeriklerinin Tespiti

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