{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T11:08:44Z","timestamp":1774264124743,"version":"3.50.1"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T00:00:00Z","timestamp":1612224000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T00:00:00Z","timestamp":1612224000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s42979-021-00458-2","type":"journal-article","created":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T14:35:40Z","timestamp":1612276540000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Red Blood Cell Classification Using Image Processing and CNN"],"prefix":"10.1007","volume":"2","author":[{"given":"Mamata Anil","family":"Parab","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3037-5076","authenticated-orcid":false,"given":"Ninad Dileep","family":"Mehendale","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"issue":"1","key":"458_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1182\/blood.V88.1.3.3","volume":"88","author":"JN George","year":"1996","unstructured":"George JN, Woolf SH, Raskob GE, Wasser J, Aledort L, Ballem P, Blanchette V, Bussel J, Cines D, Kelton J, et al. Idiopathic thrombocytopenic purpura: a practice guideline developed by explicit methods for the American Society of Hematology. Blood. 1996;88(1):3.","journal-title":"Blood."},{"key":"458_CR2","volume-title":"Peripheral blood smear-clinical methods: the history, physical, and laboratory examinations","author":"HK Walker","year":"1990","unstructured":"Walker HK, Hall WD, Hurst JW. Peripheral blood smear-clinical methods: the history, physical, and laboratory examinations. Boston: Butterworths; 1990."},{"key":"458_CR3","doi-asserted-by":"crossref","unstructured":"Teitel Pa. Basic principles of the \u2018Filterability test\u2019(FT) and analysis of erythrocyte flow behavior. In: Red Cell Rheology, pp. 55\u201370. Springer, Berlin, Heidelberg, 1978.","DOI":"10.1007\/978-3-642-67059-6_4"},{"issue":"2","key":"458_CR4","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/S0262-8856(01)00092-0","volume":"20","author":"C Di Ruberto","year":"2002","unstructured":"Di Ruberto C, Dempster A, Khan S, Jarra B. Analysis of infected blood cell images using morphological operators. Image Vis Comput. 2002;20(2):133.","journal-title":"Image Vis Comput"},{"key":"458_CR5","unstructured":"Cai R, Qingxiang W, Rui Z, Lijuan F, Chengmei R. Red blood cell segmentation using Active Appearance Model. In 2012 IEEE 11th International conference on signal processing, vol. 3, pp. 1641\u20131644. IEEE, 2012."},{"issue":"7","key":"458_CR6","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1016\/j.micron.2010.04.017","volume":"41","author":"M Ghosh","year":"2010","unstructured":"Ghosh M, Das D, Chakraborty C, Ray AK. Automated leukocyte recognition using fuzzy divergence. Micron. 2010;41(7):840.","journal-title":"Micron"},{"key":"458_CR7","unstructured":"Sinha N, Ramakrishnan AG. Automation of differential blood count. In: TENCON 2003 Conference on convergent technologies for Asia-Pacific Region, vol. 2, pp. 547\u2013551. IEEE, 2003."},{"key":"458_CR8","unstructured":"Piuri V, Fabio S. Morphological classification of blood leucocytes by microscope images. In: 2004 IEEE international conference on computational intelligence for measurement systems and applications, 2004. CIMSA., pp. 103\u2013108. IEEE, 2004."},{"key":"458_CR9","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.procs.2014.11.053","volume":"42","author":"R Tomari","year":"2014","unstructured":"Tomari R, Zakaria WNW, Jamil MMA, Nor FM, Fuad NFN. Computer aided system for red blood cell classification in blood smear image. Procedia Comput Sci. 2014;42:206.","journal-title":"Procedia Comput Sci"},{"key":"458_CR10","unstructured":"Justus D, John B, Stephen B, Andrew SM. Predicting the computational cost of deep learning models. In: 2018 IEEE international conference on big data (Big Data), pp. 3873\u20133882. IEEE, 2018."},{"key":"458_CR11","unstructured":"Qiu, W, Jiaming G, Xiang L, Mengjia X, Mo Z, Ning G, Quanzheng L. Multi-label detection and classification of red blood cells in microscopic images. (2019). arXiv:1910.02672."},{"key":"458_CR12","unstructured":"Khameneh NB, Hossein A, Piruz S, Saeed S. Abnormal red blood cells detection using adaptive neurofuzzy system. In: Mmvr, pp. 30\u201334. 2012"},{"issue":"4","key":"458_CR13","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1049\/htl.2018.5098","volume":"6","author":"MM Alam","year":"2019","unstructured":"Alam MM, Islam MT. Machine learning approach of automatic identification and counting of blood cells. Healthc Technol Lett. 2019;6(4):103.","journal-title":"Healthc Technol Lett"},{"key":"458_CR14","doi-asserted-by":"crossref","unstructured":"Rahmat RF, Wulandari FS, Faza S, Muchtar MA, Siregar I. The morphological classification of normal and abnormal red blood cell using self organizing map. In: IOP Conference Series: Mater Science Engneering vol. 308, p. 012015. 2018.","DOI":"10.1088\/1757-899X\/308\/1\/012015"},{"key":"458_CR15","doi-asserted-by":"crossref","unstructured":"Doan M, Sebastian JA, Pinto RN, McQuin C, Goodman A, Wolkenhauer O,  Parsons MJ et al. Label-free assessment of red blood cell storage lesions by deep learning. BioRxiv 2018;256180.","DOI":"10.1101\/256180"},{"key":"458_CR16","doi-asserted-by":"crossref","unstructured":"Singh I, Pal Singh N, Singh H, Bawankar S, Ngom A. Blood cell types classification using CNN. In: International work-conference on bioinformatics and biomedical engineering, Springer, Cham, 2020. pp. 727\u2013738.","DOI":"10.1007\/978-3-030-45385-5_65"},{"key":"458_CR17","unstructured":"Ferreira RL, Coelho Naldi M, Fernando Mari J. Morphological analysis and classification of erythrocytes in microscopy images. In: Proceedings of the 2016 workshop de vis\u00e3o computacional, Campo Grande, Brazil, 2016. pp. 9-11"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00458-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s42979-021-00458-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00458-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T14:27:05Z","timestamp":1617200825000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s42979-021-00458-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,2]]},"references-count":17,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["458"],"URL":"https:\/\/doi.org\/10.1007\/s42979-021-00458-2","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.05.16.087239","asserted-by":"object"}]},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"value":"2662-995X","type":"print"},{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,2]]},"assertion":[{"value":"24 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"Authors M. Parab and N. Mehendale declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies with animals or humans performed by any of the authors. And also this article contains the study on images of human blood samples. All the necessary permissions were obtained from the Institute Ethical Committee and concerned authorities.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Involvement of Human Participant and Animals"}},{"value":"Informed consent was obtained from all the human participants whose blood slide images were used for image processing.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Information About Informed Consent"}}],"article-number":"70"}}