Microscopic images dataset for automation of RBCs counting

Abbas, Sherif

doi:10.1016/j.dib.2015.08.006

Cited by 11 publications

(4 citation statements)

References 4 publications

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“…Image processing and computer vision methods are utilized for counting purposes. Isolated blood cells are counted via automated algorithms rather than manual, which enhances accuracy ( Abbas, 2015 ; Bhavnani, Jaliya & Joshi, 2016 ; Miao & Xiao, 2018 ).…”

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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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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“…Biological cell counting is an important method to evaluate the progress of cell proliferation and the diagnosis of various diseases. Automated cell counting using software based morphological analysis has been employed in several biological and medical applications including: red blood cell (RBC) counting, white blood cells (WBC) counting, embryonic stem cell counting and tumour detection in mammography (Abbas, 2015;Chourasiya et al, 2014;Joshi et al, 2013;Hsu, 2012;Ghosh et al, 2016, Sandhaus, 2015Yi et al, 2013, Shirazi et al, 2016Tomari et al, 2014;Faustino et al, 2009).…”

Section: Automated Counting Of Biological Cellsmentioning

confidence: 99%

“…Most of the biological cell counting applications start with a colour image acquired under a light microscope. This includes RBCs and WBCs cell counting applications (Abbas, 2015;Chourasiya et al, 2014;Joshi et al, 2013;Ghosh et al, 2016;Yi et al 2013). A colour image in its standard form consists of three layers of colours: red, green and blue (RGB).…”

Section: Automated Counting Of Biological Cellsmentioning

confidence: 99%

See 1 more Smart Citation

Automated Identification and Counting of Proliferating Mesenchymal Stem Cells in Bone Callus

Awad

Abdallat

Smadi

et al. 2019

IJCVR

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The assessment of cell count is essential for the evaluation of biological cell proliferation development. Manual counting can be time consuming and subject to human error as it depends on visual inspection. On the other hand, automated counting using software based morphological analysis can eliminate or reduce these disadvantages and provide statistical reliability. In this study, we employ a software-based method for the automated counting of mesenchymal stem cells (MSCs) proliferation in the bone callus of Wistar rats to evaluate fracture healing. The proposed method started with extracting the green component of the digital image acquired using a light microscope. The subsequent stages involved: contrast enhancement, adaptive thresholding and false detection reduction. This method was tested using 48 MSCs images and the results were evaluated by a specialist. The average of precision, recall and F-measure were found to be 87.14%, 88.04% and 87.50% respectively.

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