Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices

Xia, Tiancheng; Jiang, Richard; Fu, Yong Qing; Jin, Nanlin

doi:10.1088/1757-899x/646/1/012048

Cited by 25 publications

(11 citation statements)

References 18 publications

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“…The study mentioned that the entirely fine-tuned model was able to achieve an mAP of 0.8457, considering all the background noise in the images and avoiding all the unwanted small artifacts in the images. Earlier, Faster RCNN was implemented for automated blood cell detection and counting 30 . The authors have mentioned achieving a detection accuracy of 0.984, which is comparatively very much close to our results.…”

Section: Discussionmentioning

confidence: 99%

Automated detection of patterned single-cells within hydrogel using deep learning

Debnath

Hattori

Okamoto

et al. 2022

Sci Rep

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Single-cell analysis has been widely used in various biomedical engineering applications, ranging from cancer diagnostics, and immune response monitoring to drug screening. Single-cell isolation is fundamental for observing single-cell activities and an automatic finding method of accurate and reliable cell detection with few possible human errors is also essential. This paper reports trapping single cells into photo patternable hydrogel microwell arrays and isolating them. Additionally, we present an object detection-based DL algorithm that detects single cells in microwell arrays and predicts the presence of cells in resource-limited environments at the highest possible mAP (mean average precision) of 0.989 with an average inference time of 0.06 s. This algorithm leads to the enhancement of the high-throughput single-cell analysis, establishing high detection precision and reduced experimentation time.

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Section: Discussionmentioning

confidence: 99%

Automated detection of patterned single-cells within hydrogel using deep learning

Debnath

Hattori

Okamoto

et al. 2022

Sci Rep

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“…There are 1,3,5,7,9,13,17,20, and 23 to be chosen. Four different combinations including [1,3,5] , [3,5,7], [5,7,9], [7,9,13], [9,13,17], [13,17,20] were chosen to be studied.…”

Section: Modification Of Model Structurementioning

confidence: 99%

Blood Cell Count and Detection Method Based on YOLO

Liu¹

2022

HSET

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Blood Cell Count and Detection (BCCD) has always been a popular topic in object detection and many researchers have applied and modified the two basic models: Faster RCNN and Yolo. However, it is still difficult to tell which model or modification would perform better on other BCCD datasets. Thus, this paper mainly focuses on finding a better model and modifications to BCCD example datasets containing 364 images of blood cells. Faster RCNN and Yolo v5 were used as the basic two models for the dataset. Through training and comparisons between the two models, the better model was chosen to make further modifications or adjustments to achieve a better maP result possible. The result shows that in this specific dataset, Yolo v5 performs better. The modified Yolo v5 model also has an improvement of 0.6 percent of map 0.5 and 0.5 percent of map 0.95 comparing to the original model, showing that modification of model configuration, model structures including head and backbone would efficiently improve the time taken for training and maP.

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“…Though there are different laboratory analysis techniques of blood examination available, image processing and computer vision could play a sound and satisfactory role in identifying maladies, preferably it analyzes the morphological abnormality of different components of blood ( Razzak, 2017 ; Loddo, Ruberto & Putzu, 2016 ). The following are various areas where image processing and computer vision could be utilized for blood cell analysis ( Othman, Mohammed & Ali, 2017 ; Alom et al, 2018 ; Lavanya & Sushritha, 2017 ; Xia et al, 2019 ).…”

Section: Review Overviewmentioning

confidence: 99%

A review of microscopic analysis of blood cells for disease detection with AI perspective

Deshpande

Gite

Aluvalu³

2021

PeerJ Computer Science

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Background Any contamination in the human body can prompt changes in blood cell morphology and various parameters of cells. The minuscule images of blood cells are examined for recognizing the contamination inside the body with an expectation of maladies and variations from the norm. Appropriate segmentation of these cells makes the detection of a disease progressively exact and vigorous. Microscopic blood cell analysis is a critical activity in the pathological analysis. It highlights the investigation of appropriate malady after exact location followed by an order of abnormalities, which assumes an essential job in the analysis of various disorders, treatment arranging, and assessment of results of treatment. Methodology A survey of different areas where microscopic imaging of blood cells is used for disease detection is done in this paper. Research papers from this area are obtained from a popular search engine, Google Scholar. The articles are searched considering the basics of blood such as its composition followed by staining of blood, that is most important and mandatory before microscopic analysis. Different methods for classification, segmentation of blood cells are reviewed. Microscopic analysis using image processing, computer vision and machine learning are the main focus of the analysis and the review here. Methodologies employed by different researchers for blood cells analysis in terms of these mentioned algorithms is the key point of review considered in the study. Results Different methodologies used for microscopic analysis of blood cells are analyzed and are compared according to different performance measures. From the extensive review the conclusion is made. Conclusion There are different machine learning and deep learning algorithms employed by researchers for segmentation of blood cell components and disease detection considering microscopic analysis. There is a scope of improvement in terms of different performance evaluation parameters. Different bio-inspired optimization algorithms can be used for improvement. Explainable AI can analyze the features of AI implemented system and will make the system more trusted and commercially suitable.

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Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices

Cited by 25 publications

References 18 publications

Automated detection of patterned single-cells within hydrogel using deep learning

Automated detection of patterned single-cells within hydrogel using deep learning

Blood Cell Count and Detection Method Based on YOLO

A review of microscopic analysis of blood cells for disease detection with AI perspective

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