Diagnosis of Diabetes Mellitus by Extraction of Morphological Features of Red Blood Cells Using an Artificial Neural Network

2016 ◽  
Vol 124 (09) ◽  
pp. 548-556
Author(s):  
Vinupritha Palanisamy ◽  
Anburajan Mariamichael
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Artificial Neural Network ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Morphological Features ◽  
Artificial Neural ◽  
Diagnosis Of Diabetes Mellitus

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.

scholarly journals Application of Artificial Neural Network Method as A Detection of Blood Fat Disorders in Images of Complete Blood Examination

2021 ◽  
Vol 2 (2) ◽  
pp. 27
Author(s):  
Catharina Natasa Bella Fortuna ◽  
Franky Chandra Satria Arisgraha, S.T., M.T. ◽  
Puspa Erawati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Blood Lipid ◽  
Blood Examination ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Lipid Abnormalities

Based on various epidemiological studies, it is stated that blood lipids are the main risk factor for atherosclerosis that leads to coronary heart disease. In patients with blood lipid disorders, red blood cells undergo deformability so that their shape is flatter than normal red blood cells, which are round. The research entitled Application of Artificial Neural Network Method as Detection of Blood Fat Abnormalities in Image of Complete Blood Examination Results was conducted to help facilitate laboratory examinations. This research hopes that it will provide appropriate early detection to support the expert diagnosis. This research consists of two stages. The first stage is digital image processing to obtain area, perimeter, and eccentricity features. These three features will be used as input to the Backpropagation Neural Network program as the second stage. At this stage, blood lipid abnormalities are detected from features that have been obtained from image processing. The accuracy of detecting blood lipid abnormalities with ANN Backpropagation is 85%.

scholarly journals Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study

2019 ◽  
Vol 4 (2) ◽  
pp. 33
Author(s):  
Shehu Usman Gulumbe ◽  
Shamsuddeen Suleiman ◽  
Shehu Badamasi ◽  
Ahmad Yusuf Tambuwal ◽  
Umar Usman
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Artificial Neural Network ◽  
Simulation Study ◽  
Artificial Neural
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