YOLOv5 based detector for eight different urine particles components on single board computer

Çınar, Ahmet; Erkuş, Merve; Tuncer, Taner; Ayyıldız, Hakan; Tuncer, Seda Arslan

doi:10.1002/ima.22968

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Known for its efficiency in classifying and locating objects in a single step, the model has garnered sustained attention since the release of YOLOv3 [20][21][22], with ongoing improvements in its performance. Currently, in the field of object detection, YOLOv5 [23,24] and YOLOv7 [25] stand as the two dominant algorithms. Developed by the Ultralytics team, YOLOv5 excels in both speed and accuracy, making it widely adopted in industrial applications.…”

Section: The Yolo Familymentioning

confidence: 99%

SOCR‐YOLO: Small Objects Detection Algorithm in Medical Images

Liu,

Li,

Jiang

et al. 2024

Int J Imaging Syst Tech

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In the field of medical image analysis, object detection plays a crucial role by providing interpretable diagnostic information to healthcare professionals. Although current object detection models have achieved remarkable success in conventional images, their performance in detecting abnormalities in medical images has not been as satisfactory. This is primarily due to the complexity of anatomical structures in medical images, and the fact that some lesions may have subtle features, particularly in the case of early‐stage, small‐scale abnormalities. To address this challenge, we introduce SOCR‐YOLO, a novel lesion detection model with online convolutional reparameterization based on channel shuffling. First, it employs the SOCR (Shuffled Channel with Online Convolutional Re‐parameterization) module to establish a connection between feature concatenation and computational efficiency, aiming to extract more comprehensive information while reducing time consumption. Second, it incorporates the Bi‐FPN structure to achieve multiscale feature fusion. Lastly, the loss function has been optimized to improve the model training process. We evaluated two datasets, chest x‐ray (Vindr‐CXR) and brain tumor (Br35H), provided by the Kaggle competition. Experimental results show that the proposed method has outperformed several state‐of‐the‐art models, including YOLOv8, YOLO‐NAS, and RT‐DETR, in both speed and accuracy. Notably, in the context of chest x‐ray anomaly detection, SOCR‐YOLO exhibits a 1.8% enhancement in accuracy over YOLOv8 while simultaneously reducing floating‐point operations by 26.3%. Additionally, a similar 1.8% improvement in accuracy is observed in the detection of brain tumors. The results indicate the superior ability of our model to detect multiscale variations and small lesions.

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Section: The Yolo Familymentioning

confidence: 99%

SOCR‐YOLO: Small Objects Detection Algorithm in Medical Images

Liu,

Li,

Jiang

et al. 2024

Int J Imaging Syst Tech

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Deep Multi-Modal Fusion Model for Identification of Eight Different Particles in Urinary Sediment

Tuncer,

Çınar,

Erkuş

et al. 2024

Applied Computer Systems

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Urine sediment examination (USE) is an essential aspect in detecting urinary system diseases, and it is a prerequisite for diagnostic procedures. Urine images are complex, containing numerous particles, which makes a detailed analysis and interpretation challenging. It is crucial for both patients and medical professionals to conduct urine analysis automatically, quickly and inexpensively, without compromising reliability. In this paper, we present a deep multi-modal fusion system, commonly employed in artificial intelligence, capable of automatically distinguishing particles in urine sediment. To achieve this objective, we first created a new dataset comprising erythrocytes, leukocytes, yeast, epithelium, bacteria, crystals, cylinders, and other particles (such as sperm). The data were gathered from urinalysis requests made between July 2022 and September 2022 at the biochemistry laboratory of Fethi Sekin Medical Center Hospital. A dataset containing 8509 images was compiled using the Optika B293PLi microscope with trinocular brightfield. We propose a 5-step process for detecting particles in the dataset using a multi-modal fusion deep learning model: i) The obtained images were augmented by applying affine transformation. ii) To distinguish images, we opted for ResNet18 and ResNet50 models, which yielded high performance in medical data. iii) Feature vectors from both models were fused to generate more consistent, accurate, and useful particle features. iv) We employed ReliefF, Neighborhood Component Analysis (NCA), and Minimum-Redundancy Maximum-Relevancy (mRMR) feature selection methods, widely used to determine features that maximise particle discrimination success. v) In the final step, Support Vector Machine (SVM) was utilised to distinguish the particles. The results demonstrate that the highest accuracy value achieved is 98.54 % when employing the ReliefF algorithm. Contributions of the study include eliminating standardisation differences in manual microscopy, achieving high accuracy in particle discrimination, offering an artificial intelligence-based system applicable in laboratory environments, and providing the dataset as educational and practical material for biochemistry professionals.

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YOLOv5 based detector for eight different urine particles components on single board computer

Cited by 2 publications

References 24 publications

SOCR‐YOLO: Small Objects Detection Algorithm in Medical Images

SOCR‐YOLO: Small Objects Detection Algorithm in Medical Images

Deep Multi-Modal Fusion Model for Identification of Eight Different Particles in Urinary Sediment

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