scholarly journals Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network

2020 ◽  
Vol 25 (11) ◽  
Author(s):  
Yang-Hsien Lin ◽  
Ken Y.-K. Liao ◽  
Kung-Bin Sung
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Automatic Detection ◽  
Quantitative Phase ◽  
Phase Images

scholarly journals A deep convolutional neural network for classification of red blood cells in sickle cell anemia

2017 ◽  
Vol 13 (10) ◽  
pp. e1005746 ◽  
Author(s):  
Mengjia Xu ◽  
Dimitrios P. Papageorgiou ◽  
Sabia Z. Abidi ◽  
Ming Dao ◽  
Hong Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Red Blood Cells ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Deep Convolutional Neural Network

scholarly journals Implementation of Convolutional Neural Network in the classification of red blood cells have affected of malaria

SinkrOn ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 199-207
Author(s):  
Mawaddah Harahap ◽  
Jefferson Jefferson ◽  
Surya Barti ◽  
Suprianto Samosir ◽  
Christi Andika Turnip
Keyword(s):  
Neural Network ◽  
Blood Cell ◽  
Convolutional Neural Network ◽  
Red Blood Cells ◽  
Red Blood Cell ◽  
Blood Cells ◽  
Training Data ◽  
Test Accuracy ◽  
Number Of Layers

Malaria is a disease caused by plasmodium which attacks red blood cells. Diagnosis of malaria can be made by examining the patient's red blood cells using a microscope. Convolutional Neural Network (CNN) is a deep learning method that is growing rapidly. CNN is often used in image classification. The CNN process usually requires considerable resources. This is one of the weaknesses of CNN. In this study, the CNN architecture used in the classification of red blood cell images is LeNet-5 and DRNet. The data used is a segmented image of red blood cells and is secondary data. Before conducting the data training, data pre-processing and data augmentation from the dataset was carried out. The number of layers of the LeNet-5 and DRNet models were 4 and 7. The test accuracy of the LeNet-5 and DrNet models was 95% and 97.3%, respectively. From the test results, it was found that the LeNet-5 model was more suitable in terms of red blood cell classification. By using the LeNet-5 architecture, the resources used to perform classification can be reduced compared to previous studies where the accuracy obtained is also the same because the number of layers is less, which is only 4 layers

scholarly journals Melittin-induced alterations in morphology and deformability of human red blood cells using quantitative phase imaging techniques

2016 ◽  
Author(s):  
Joonseok Hur ◽  
Kyoohyun Kim ◽  
SangYun Lee ◽  
HyunJoo Park ◽  
YongKeun Park
Keyword(s):  
Refractive Index ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Imaging Techniques ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Active Molecule ◽  
Quantitative Phase ◽  
Human Red Blood Cells ◽  
Phase Images

Here, the actions of melittin, the active molecule of apitoxin or bee venom, were investigated on human red blood cells (RBCs) using quantitative phase imaging techniques. High-resolution realtime 3-D refractive index (RI) measurements and dynamic 2-D phase images of individual melittin-bound RBCs enabled in-depth examination of melittin-induced biophysical alterations of the cells. From the measurements, morphological, biochemical, and mechanical alterations of the RBCs were analyzed quantitatively. Furthermore, leakage of haemoglobin (Hb) inside the RBCs at high melittin concentration was also investigated.

scholarly journals Automatic Detection and Classification of White Blood Cells in Blood Smear Images Using Convolutional Neural Network

2019 ◽  
Vol 16 (Special Issue) ◽  
Author(s):  
Ramin Nateghi ◽  
Mansoor Fatehi ◽  
Ali Sadeghitabar ◽  
Romana Khosravi ◽  
Fattane Pourakpour
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Blood Cells ◽  
Blood Smear ◽  
White Blood Cells ◽  
Automatic Detection
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