کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7229796 | 1470929 | 2018 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning
ترجمه فارسی عنوان
سنسور رایگان برای شناسایی خودکار اریتروسیتها با استفاده از میکروسکوپ هولوگرافی دیجیتال و یادگیری ماشین
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کلمات کلیدی
طبقه بندی نوع سلول، اریتروسیت، میکروسکوپ هولوگرافی دیجیتال در خط، فراگیری ماشین،
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
چکیده انگلیسی
Cell types of erythrocytes should be identified because they are closely related to their functionality and viability. Conventional methods for classifying erythrocytes are time consuming and labor intensive. Therefore, an automatic and accurate erythrocyte classification system is indispensable in healthcare and biomedical fields. In this study, we proposed a new label-free sensor for automatic identification of erythrocyte cell types using a digital in-line holographic microscopy (DIHM) combined with machine learning algorithms. A total of 12 features, including information on intensity distributions, morphological descriptors, and optical focusing characteristics, is quantitatively obtained from numerically reconstructed holographic images. All individual features for discocytes, echinocytes, and spherocytes are statistically different. To improve the performance of cell type identification, we adopted several machine learning algorithms, such as decision tree model, support vector machine, linear discriminant classification, and k-nearest neighbor classification. With the aid of these machine learning algorithms, the extracted features are effectively utilized to distinguish erythrocytes. Among the four tested algorithms, the decision tree model exhibits the best identification performance for the training sets (n = 440, 98.18%) and test sets (n = 190, 97.37%). This proposed methodology, which smartly combined DIHM and machine learning, would be helpful for sensing abnormal erythrocytes and computer-aided diagnosis of hematological diseases in clinic.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biosensors and Bioelectronics - Volume 103, 30 April 2018, Pages 12-18
Journal: Biosensors and Bioelectronics - Volume 103, 30 April 2018, Pages 12-18
نویسندگان
Taesik Go, Hyeokjun Byeon, Sang Joon Lee,