Overview on convolutional neural network-based classification of red blood cells in lensless single random phase encoding

2021 ◽  
Author(s):  
Timothy O’Connor ◽  
Christopher Hawxhurst ◽  
Leslie M. Shor ◽  
Bahram Javidi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Random Phase ◽  
Phase Encoding ◽  
Random Phase Encoding

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 Classification of White Blood Cells Using Convolutional Neural Network

2018 ◽  
Vol 21 (1) ◽  
pp. 65-80
Author(s):  
Amin Edraki ◽  
AbolHassan Razminia ◽  
◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Blood Cells ◽  
White Blood Cells

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

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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