دورية أكاديمية
Enhancing blood flow prediction in multi-exposure laser speckle contrast imaging through ensemble learning with K-mean clustering.
| العنوان: | Enhancing blood flow prediction in multi-exposure laser speckle contrast imaging through ensemble learning with K-mean clustering. |
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| المؤلفون: | Jain P; National Institute of Technology Raipur, Raipur, CG, 492010, India., Gupta S; National Institute of Technology Raipur, Raipur, CG, 492010, India. |
| المصدر: | Biomedical physics & engineering express [Biomed Phys Eng Express] 2024 Jan 04; Vol. 10 (2). Date of Electronic Publication: 2024 Jan 04. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: IOP Publishing Ltd Country of Publication: England NLM ID: 101675002 Publication Model: Electronic Cited Medium: Internet ISSN: 2057-1976 (Electronic) Linking ISSN: 20571976 NLM ISO Abbreviation: Biomed Phys Eng Express Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Bristol : IOP Publishing Ltd., [2015]- |
| مواضيع طبية MeSH: | Laser Speckle Contrast Imaging* , Hemodynamics*, Humans ; Algorithms ; Machine Learning ; Chronic Disease |
| مستخلص: | Purpose. Accurately visualizing and measuring blood flow is of utmost importance in maintaining optimal health and preventing the onset of various chronic diseases. One promising imaging technique that aids in visualizing perfusion in biological tissues is Multi-exposure Laser Speckle Contrast Imaging (MELSCI). MELSCI technique allows real-time quantitative measurements using multiple exposure times to obtain precise and reliable blood flow data. Additionally, the application of machine learning (ML) techniques can further enhance the accuracy of blood flow prediction in this imaging modality. Method. Our study focused on developing and evaluating Ensemble Learning ML techniques along with clustering algorithms for predicting blood flow rates in MELSCI. The effectiveness of these techniques was assessed using performance parameters, including accuracy, F1-score, precision, recall, specificity, and classification error rate. Result. Notably, the study revealed that Ensemble Learning with clustering emerged as the most accurate technique, achieving an impressive accuracy rate of 98.5%. Furthermore, it demonstrated a high recall of more than 91%, F1-score, the precision of more than 90%, higher specificity of 99%, and least classification error of 1.5%, highlighting its suitability and sustainability for flow prediction in MELSCI. Conclusion. The study's findings imply that Ensemble Learning can significantly contribute to enhancing the accuracy of blood flow prediction in MELSCI. This advancement holds substantial promise for healthcare professionals and researchers, as it facilitates improved understanding and assessment of perfusion within biological tissues, which will contribute to the maintenance of good health and prevention of chronic diseases. (© 2024 © 2022 IOP Publishing Ltd.) |
| فهرسة مساهمة: | Keywords: K-mean clustering; ensemble learning; flow prediction; laser speckle contrast imaging; machine learning; multi-exposure laser speckle contrast imaging |
| تواريخ الأحداث: | Date Created: 20231218 Date Completed: 20240105 Latest Revision: 20240105 |
| رمز التحديث: | 20240105 |
| DOI: | 10.1088/2057-1976/ad16c2 |
| PMID: | 38109789 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2057-1976 |
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| DOI: | 10.1088/2057-1976/ad16c2 |