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Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network
المؤلفون المشاركون
Almezhghwi, Khaled
Serte, Sertan
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-09
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements.
The five types of white blood cells include neutrophils, eosinophils, lymphocytes, monocytes, and basophils, where each type constitutes a different proportion and performs specific functions.
Being able to classify and, therefore, count these different constituents is critical for assessing the health of patients and infection risks.
Generally, laboratory experiments are used for determining the type of a white blood cell.
The staining process and manual evaluation of acquired images under the microscope are tedious and subject to human errors.
Moreover, a major challenge is the unavailability of training data that cover the morphological variations of white blood cells so that trained classifiers can generalize well.
As such, this paper investigates image transformation operations and generative adversarial networks (GAN) for data augmentation and state-of-the-art deep neural networks (i.e., VGG-16, ResNet, and DenseNet) for the classification of white blood cells into the five types.
Furthermore, we explore initializing the DNNs’ weights randomly or using weights pretrained on the CIFAR-100 dataset.
In contrast to other works that require advanced image preprocessing and manual feature extraction before classification, our method works directly with the acquired images.
The results of extensive experiments show that the proposed method can successfully classify white blood cells.
The best DNN model, DenseNet-169, yields a validation accuracy of 98.8%.
Particularly, we find that the proposed approach outperforms other methods that rely on sophisticated image processing and manual feature engineering.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Almezhghwi, Khaled& Serte, Sertan. 2020. Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138785
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Almezhghwi, Khaled& Serte, Sertan. Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1138785
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Almezhghwi, Khaled& Serte, Sertan. Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138785
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1138785
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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