scholarly journals DETECTION OF BLOOD CELLS

2019 ◽  
Vol 1 (92) ◽  
pp. 26-30
Author(s):  
G.Yu. Tereshchenko ◽  
G.G. Chetverykov ◽  
I. Konarieva
Keyword(s):  
Blood Cells ◽  
Medical Image ◽  
Morphological Analysis ◽  
Medical Image Analysis ◽  
Recognition System ◽  
Image Analysis System ◽  
Cell Recognition ◽  
Threshold Values ◽  
Step Algorithm ◽  
Analysis System

The structure of the medical image analysis system is considered. The algorithm of the blood cell recognition system is given. Formulated the main tasks to be solved during the morphological analysis of blood. The requirements for the algorithm in determining the leukocyte formula and the detection of blood corpuscles on a smear were determined. A model of color-brightness characteristics is proposed for describing typical images of a blood smear. The threshold values of the sizes of objects are determined when searching for cells. A histogram of the brightness of a typical field of view was investigated. A two-step algorithm for detecting blood cells is described, as well as an algorithm for constructing a dividing line on the plane of relative colors. The results of experiments on real preparations are given. The causes of detection errors are considered.

Study on the Regulation of Compound siRNA Nanoparticles on the Rat Model of Kidney Injury Induced by Sepsis by Inhibiting the Expression of NF-κB and P65

2021 ◽  
Vol 21 (2) ◽  
pp. 1345-1350
Author(s):  
Ye Chen ◽  
Xiaoxia Liu ◽  
Meiling Chen ◽  
Run Yan ◽  
Wenyu Song
Keyword(s):  
Rat Model ◽  
Group Treatment ◽  
Medical Image Analysis ◽  
Intervention Group ◽  
Kidney Tissue ◽  
Kidney Injury ◽  
Control Group ◽  
Image Analysis System ◽  
Model Group ◽  
Analysis System

This article explores the pathogenesis of sepsis AKI, and seeks to protect the acute damage of sepsis tissues and organs. This study is to prepare a rat sepsis-induced AKI model by CLP, and to observe the pathological changes of kidney tissue and the function of kidney changes, and observe the effect of siRNA nanoparticles on its intervention, preliminary explore the protective effect and possible mechanism of siRNA nanoparticles on AKI in sepsis rats, and provide more information for the clinical treatment of siRNA nanoparticles in sepsis theoretical and experimental basis. We analysis the benefit and deficiency of nuclear factor-κB (NF-κB) activation in the pathogenesis of glomerulonephritis and its regulatory effect on NF-κB activation. In the rat model group, no treatment was given after injection of nephrotoxic serum, and the rats were sacrificed on the 14th day; the compound siRNA nanoparticle intervention group (treatment group) was given dexamethasone 0.125 daily on the 1st to 14th day after nephrotoxic serum injection. Immunohistochemistry and medical image analysis system were used to observe NF-κB activation of monocyte chemotactic protein-1 (MCP-1) in glomeruli and tubules, and analyze their relationship with proteinuria and glomerular cells. The results showed that the expression of NF-κB in the glomeruli and tubules of the model group was significantly up-regulated regarding to the control group, and MCP-1’s expression in the glomeruli and tubules of the model group was higher than that of the control group. The activation of NF-κB and the expression of MCP-1 in glomeruli are closely related to monocyte infiltration and proteinuria; NF-κB activation and MCP-1 expression in glomeruli and tubules of the compound siRNA nanoparticles intervention group were significantly down-regulated. It was concluded that the activation of NF-κB has great impact on the pathogenesis of glomerulonephritis, and inhibition of NF-κB activation may be one of the mechanisms of anti-nephritis effect.

scholarly journals Deep ensemble learning for skin lesions classification with convolutional neural network

2021 ◽  
Vol 10 (3) ◽  
pp. 563
Author(s):  
Renny Amalia Pratiwi ◽  
Siti Nurmaini ◽  
Dian Palupi Rini ◽  
Muhammad Naufal Rachmatullah ◽  
Annisa Darmawahyuni
Keyword(s):  
Skin Cancer ◽  
Ensemble Learning ◽  
Medical Image Analysis ◽  
Skin Lesions ◽  
Classification Performance ◽  
Image Analysis System ◽  
Deep Convolutional Neural Networks ◽  
Good Classification Performance ◽  
Analysis System ◽  
Sensitivity Specificity

<span lang="EN-US">One type of skin cancer that is considered a malignant tumor is melanoma. Such a dangerous disease can cause a lot of death in the world. The early detection of skin lesions becomes an important task in the diagnosis of skin cancer. Recently, a machine learning paradigm emerged known as deep learning (DL) utilized for skin lesions classification. However, in some previous studies by using seven class images diagnostic of skin lesions classification based on a single DL approach with CNNs architecture does not produce a satisfying performance. The DL approach allows the development of a medical image analysis system for improving performance, such as the deep convolutional neural networks (DCNNs) method. In this study, we propose an ensemble learning approach that combines three DCNNs architectures such as Inception V3, Inception ResNet V2 and DenseNet 201 for improving the performance in terms of accuracy, sensitivity, specificity, precision, and F1-score. Seven classes of dermoscopy image categories of skin lesions are utilized with 10015 dermoscopy images from well-known the HAM10000 dataset. The proposed model produces good classification performance with 97.23% accuracy, 90.12% sensitivity, 97.73% specificity, 82.01% precision, and 85.01% F1-Score. This method gives promising results in classifying skin lesions for cancer diagnosis.</span>

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