Application of ensemble artificial neural network for the classification of white blood cells using microscopic blood images

2018 ◽  
Vol 4 (2/3) ◽  
pp. 202
Author(s):  
H.S. Bhadauria ◽  
Jagtar Singh Devgun ◽  
Jitendra Virmani ◽  
Jyoti Rawat ◽  
Annapurna Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Blood Cells ◽  
White Blood Cells ◽  
Artificial Neural

scholarly journals Classification of acute myeloid leukemia subtypes M1, M2 and M3 using active contour without edge segmentation and momentum backpropagation artificial neural network

2018 ◽  
Vol 154 ◽  
pp. 01041 ◽  
Author(s):  
Agus Harjoko ◽  
Tri Ratnaningsih ◽  
Esti Suryani ◽  
Wiharto ◽  
Sarngadi Palgunadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acute Myeloid Leukemia ◽  
Blood Cells ◽  
Myeloid Leukemia ◽  
Active Contour ◽  
White Blood Cells ◽  
Artificial Neural ◽  
Backpropagation Artificial Neural Network ◽  
Acute Myeloid

Acute Myeloid Leukemia (AML) is a type of cancer which attacks white blood cells from myeloid. AML has eight subtypes, namely: M0, M1, M2, M3, M4, M5, M6, and M7. AML subtypes M1, M2 and M3 are affected by the same type of cells, myeloblast, making it needs more detailed analysis to distinguish. To overcome these obstacles, this research is applying digital image processing with Active Contour Without Edge (ACWE) and Momentum Backpropagation artificial neural network for AML subtypes M1, M2 and M3 classification based on the type of the cell. Six features required as training parameters from every cell obtained by using feature extraction. The features are: cell area, perimeter, circularity, nucleus ratio, mean and standard deviation. The results show that ACWE can be used for segmenting white blood cells with 83.789% success percentage of 876 total cell objects. The whole AML slides had been identified according to the cell types predicted number through training with momentum backpropagation. Five times testing calibration with the best parameter generated averages value of 84.754% precision, 75.887% sensitivity, 95.090% specificity and 93.569% accuracy.

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

Scandinavian test of artificial neural network for classification of myocardial perfusion images

2000 ◽  
Vol 20 (4) ◽  
pp. 253-261 ◽  
Author(s):  
Lindahl ◽  
Toft ◽  
Hesse ◽  
Palmer ◽  
Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Myocardial Perfusion ◽  
Artificial Neural
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