Artificial Intelligence and Data Science Methods for Automatic Detection of White Blood Cells in Images.
Data scieQuerynce (DS) methods and artificial intelligence (AI) are critical in today's healthcare services operations. This study focuses on evaluating the effectiveness of AI and DS in biomedical diagnostics, including automatic detection and counting of white blood cells (WBCs) and types, which p...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 1; pp. 583 - 604 |
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| Autores principales: | , , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191694208&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191694208 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2026 vid: 39 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191694208 191694208 191694208 10.1007/s10278-025-01538-y 191694208 ppf: 583 ppct: 21 formats: tig: atl: Artificial Intelligence and Data Science Methods for Automatic Detection of White Blood Cells in Images. aug: au: Kobara, Yawo M. Akpan, Ikpe Justice Nam, Alima Damipe AlMukthar, Firas H. Peter, Mbuotidem affil: https://ror.org/01gw3d370 Odette School of Business, University of Windsor, Windsor, ON, Canada sug: subj: Artificial Intelligence Data Science Leukocytes Research Microscopy Machine Learning Leukemia Diagnosis Hematologic Diseases Diagnosis Sensitivity and Specificity Deep Learning Diagnosis, Computer Assisted ab: Data scieQuerynce (DS) methods and artificial intelligence (AI) are critical in today's healthcare services operations. This study focuses on evaluating the effectiveness of AI and DS in biomedical diagnostics, including automatic detection and counting of white blood cells (WBCs) and types, which provide valuable information for diagnosing and treating blood diseases such as leukemia. Automating these tasks using AI and DS saves time and avoids or minimizes errors compared to manual processes, which can be complex and error prone. The study utilizes bibliographic data from SCOPUS to evaluate research on applying AI algorithms and DS methods for mapping and classifying WBC images for treatment of blood diseases, such as leukemia using literature survey and science mapping methodology. The results show the potency of different DS methods and AI algorithms, such as machine learning, deep learning, and classification algorithms that enable the automatic detection of WBC images. AI and DS algorithms offer critical benefits in effectively and efficiently analyzing microscopic images of blood cells. The automatic identification, localization, and classification of WBCs speed up the patient diagnosis process, allowing hematologists to focus on interpreting results. Automatic processes identify specific abnormalities and patterns, enhancing accuracy and timely diagnoses. Future work will examine the application of generative AI in blood cells diagnostics. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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