Applications of deep learning to the assessment of red blood cell deformability

Biorheology ◽  
2021 ◽  
pp. 1-10
Author(s):  
Alper Turgut ◽  
Özlem Yalçin
Keyword(s):  
Neural Network ◽  
Theoretical Model ◽  
Blood Cell ◽  
Convolutional Neural Network ◽  
Red Blood Cell ◽  
Network Architecture ◽  
Laser Diffraction ◽  
Cell Fraction ◽  
Cell Deformability ◽  
Diffraction Patterns

BACKGROUND: Measurement of abnormal Red Blood Cell (RBC) deformability is a main indicator of Sickle Cell Anemia (SCA) and requires standardized quantification methods. Ektacytometry is commonly used to estimate the fraction of Sickled Cells (SCs) by measuring the deformability of RBCs from laser diffraction patterns under varying shear stress. In addition to estimations from model comparisons, use of maximum Elongation Index differences (ΔEImax) at different laser intensity levels was recently proposed for the estimation of SC fractions. OBJECTIVE: Implement a convolutional neural network to accurately estimate rigid-cell fraction and RBC concentration from laser diffraction patterns without using a theoretical model and eliminating the ektacytometer dependency for deformability measurements. METHODS: RBCs were collected from control patients. Rigid-cell fraction experiments were performed using varying concentrations of glutaraldehyde. Serial dilutions were used for varying the concentration of RBC. A convolutional neural network was constructed using Python and TensorFlow. RESULTS: Our measurements and model predictions show that a linear relationship between ΔEImax and rigid-cell fraction exists only for rigid-cell fractions less than 0.2. Our proposed neural network architecture can be used successfully for both RBC concentration and rigid-cell fraction estimations without a need for a theoretical model.

Laser-diffraction slit rheometer to measure red blood cell deformability

2004 ◽  
Vol 75 (2) ◽  
pp. 559-561 ◽  
Author(s):  
S. Shin ◽  
Y. H. Ku ◽  
M. S. Park ◽  
S. Y. Moon ◽  
J. H. Jang ◽  
...  
Keyword(s):  
Blood Cell ◽  
Red Blood Cell ◽  
Laser Diffraction ◽  
Cell Deformability ◽  
Red Blood Cell Deformability

Disposable laser-diffraction ektacytometry to measure red blood cell deformability

2005 ◽  
Author(s):  
Sehyun Shin ◽  
Yunhee Ku ◽  
Myungsu Park ◽  
Lijuan Zhang ◽  
Joohee Jang ◽  
...  
Keyword(s):  
Blood Cell ◽  
Red Blood Cell ◽  
Laser Diffraction ◽  
Cell Deformability ◽  
Red Blood Cell Deformability

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 Classification of crystal structures using electron diffraction patterns with a deep convolutional neural network

RSC Advances ◽  
2021 ◽  
Vol 11 (61) ◽  
pp. 38307-38315
Author(s):  
Moonsoo Ra ◽  
Younggun Boo ◽  
Jae Min Jeong ◽  
Jargalsaikhan Batts-Etseg ◽  
Jinha Jeong ◽  
...  
Keyword(s):  
Neural Network ◽  
Electron Diffraction ◽  
Convolutional Neural Network ◽  
Crystal Structures ◽  
Network Architecture ◽  
Deep Convolutional Neural Network ◽  
Neural Network Architecture ◽  
Selected Area Electron Diffraction ◽  
Diffraction Patterns

The off-the-shelf deep convolutional neural network architecture, ResNet, could classify the space group of materials with cubic crystal structures with the prediction accuracy of 92.607%, using the selected area electron diffraction patterns.

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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