Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet

A complete blood count is one of the significant clinical tests that evaluates overall human health and provides relevant information for disease diagnosis. The conventional strategies of blood cell counting include manual counting as well as counting using the hemocytometer and are tedious and time...

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Autores principales:	Grzegorz Drałus, Damian Mazur, Anna Czmil
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	confidence threshold convolution neural networks platelet RBC WBC Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/156ea4456eed438bb1322dcfa9776064
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id	oai:doaj.org-article:156ea4456eed438bb1322dcfa9776064
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spelling	oai:doaj.org-article:156ea4456eed438bb1322dcfa97760642021-11-25T17:30:29ZAutomatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet10.3390/e231115221099-4300https://doaj.org/article/156ea4456eed438bb1322dcfa97760642021-11-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1522https://doaj.org/toc/1099-4300A complete blood count is one of the significant clinical tests that evaluates overall human health and provides relevant information for disease diagnosis. The conventional strategies of blood cell counting include manual counting as well as counting using the hemocytometer and are tedious and time-consuming tasks. This research-based paper proposes an automatic software-based alternative method to count blood cells accurately using the RetinaNet deep learning network, which is used to recognize and classify objects in microscopic images. After training, the network automatically recognizes and counts red blood cells, white blood cells, and platelets. We tested a model trained on smear images and found that the trained model has generalized capabilities. We assessed the quality of detection and cell counting using performance measures, such as accuracy, sensitivity, precision, and F1-score. Moreover, we studied the dependence of the confidence thresholds and the number of learning epochs on the obtained results of recognition and counting. We compared the performance of the proposed approach with those obtained by other authors who dealt with the subject of cell counting and show that object detection and labeling can be an additional advantage in the task of counting objects.Grzegorz DrałusDamian MazurAnna CzmilMDPI AGarticleconfidence thresholdconvolution neural networksplateletRBCWBCScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1522, p 1522 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	confidence threshold convolution neural networks platelet RBC WBC Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	confidence threshold convolution neural networks platelet RBC WBC Science Q Astrophysics QB460-466 Physics QC1-999 Grzegorz Drałus Damian Mazur Anna Czmil Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet
description	A complete blood count is one of the significant clinical tests that evaluates overall human health and provides relevant information for disease diagnosis. The conventional strategies of blood cell counting include manual counting as well as counting using the hemocytometer and are tedious and time-consuming tasks. This research-based paper proposes an automatic software-based alternative method to count blood cells accurately using the RetinaNet deep learning network, which is used to recognize and classify objects in microscopic images. After training, the network automatically recognizes and counts red blood cells, white blood cells, and platelets. We tested a model trained on smear images and found that the trained model has generalized capabilities. We assessed the quality of detection and cell counting using performance measures, such as accuracy, sensitivity, precision, and F1-score. Moreover, we studied the dependence of the confidence thresholds and the number of learning epochs on the obtained results of recognition and counting. We compared the performance of the proposed approach with those obtained by other authors who dealt with the subject of cell counting and show that object detection and labeling can be an additional advantage in the task of counting objects.
format	article
author	Grzegorz Drałus Damian Mazur Anna Czmil
author_facet	Grzegorz Drałus Damian Mazur Anna Czmil
author_sort	Grzegorz Drałus
title	Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet
title_short	Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet
title_full	Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet
title_fullStr	Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet
title_full_unstemmed	Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet
title_sort	automatic detection and counting of blood cells in smear images using retinanet
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/156ea4456eed438bb1322dcfa9776064
work_keys_str_mv	AT grzegorzdrałus automaticdetectionandcountingofbloodcellsinsmearimagesusingretinanet AT damianmazur automaticdetectionandcountingofbloodcellsinsmearimagesusingretinanet AT annaczmil automaticdetectionandcountingofbloodcellsinsmearimagesusingretinanet
_version_	1718412270098186240

Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet

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