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>

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 Can Deep Learning-Based Volumetric Analysis Predict Oxygen Demand Increase in Patients with COVID-19 Pneumonia?

Medicina ◽  
2021 ◽  
Vol 57 (11) ◽  
pp. 1148
Author(s):  
Marie Takahashi ◽  
Tomoyuki Fujioka ◽  
Toshihiro Horii ◽  
Koichiro Kimura ◽  
Mizuki Kimura ◽  
...  
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Oxygen Demand ◽  
Volumetric Analysis ◽  
Ct Scans ◽  
Image Analysis System ◽  
Volume Data ◽  
Respiratory Status ◽  
Analysis System ◽  
Sensitivity Specificity

Background and Objectives: This study aimed to investigate whether predictive indicators for the deterioration of respiratory status can be derived from the deep learning data analysis of initial chest computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19). Materials and Methods: Out of 117 CT scans of 75 patients with COVID-19 admitted to our hospital between April and June 2020, we retrospectively analyzed 79 CT scans that had a definite time of onset and were performed prior to any medication intervention. Patients were grouped according to the presence or absence of increased oxygen demand after CT scan. Quantitative volume data of lung opacity were measured automatically using a deep learning-based image analysis system. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the opacity volume data were calculated to evaluate the accuracy of the system in predicting the deterioration of respiratory status. Results: All 79 CT scans were included (median age, 62 years (interquartile range, 46–77 years); 56 (70.9%) were male. The volume of opacity was significantly higher for the increased oxygen demand group than for the nonincreased oxygen demand group (585.3 vs. 132.8 mL, p < 0.001). The sensitivity, specificity, and AUC were 76.5%, 68.2%, and 0.737, respectively, in the prediction of increased oxygen demand. Conclusion: Deep learning-based quantitative analysis of the affected lung volume in the initial CT scans of patients with COVID-19 can predict the deterioration of respiratory status to improve treatment and resource management.

scholarly journals Detection of Melanoma Skin Cancer with Deep Neural Networks

2019 ◽  
Vol 4 (4) ◽  
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
High Performance ◽  
Deep Neural Networks ◽  
Histopathological Examination ◽  
Skin Lesions ◽  
Image Features ◽  
Test Accuracy ◽  
Data Sets ◽  
Deep Convolutional Neural Networks

Detection of skin cancer involves several steps of examinations first being visual diagnosis that is followed by dermoscopic analysis, a biopsy, and histopathological examination. The classification of skin lesions in the first step is critical and challenging as classes vary by minute appearance in skin lesions. Deep convolutional neural networks (CNNs) have great potential in multicategory image-based classification by considering coarse-to-fine image features. This study aims to demonstrate how to classify skin lesions, in particular, melanoma, using CNN trained on data sets with disease labels. We developed and trained our own CNN model using a subset of the images from International Skin Imaging Collaboration (ISIC) Dermoscopic Archive. To test the performance of the proposed model, we used a different subset of images from the same archive as the test set. Our model is trained to classify images into two categories: malignant melanoma and nevus and is shown to achieve excellent classification results with high test accuracy (91.16%) and high performance as measured by various metrics. Our study demonstrated the potential of using deep neural networks to assist early detection of melanoma and thereby improve the patient survival rate from this aggressive skin cancer.

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.

scholarly journals Soft-Attention Improves Skin Cancer Classification Performance

2021 ◽  
Author(s):  
Soumyya Kanti Datta ◽  
Mohammad Abuzar Shaikh ◽  
Sargur N. Srihari ◽  
Mingchen Gao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Skin Cancer ◽  
Skin Lesions ◽  
Classification Performance ◽  
Input Image ◽  
Clinical Applications ◽  
Attention Mechanism ◽  
Original Network ◽  
Sensitivity Score

In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a neural network to achieve this goal. This paper investigates the effectiveness of Soft-Attention in deep neural architectures. The central aim of Soft-Attention is to boost the value of important features and suppress the noise-inducing features. We compare the performance of VGG, ResNet, InceptionResNetv2 and DenseNet architectures with and without the Soft-Attention mechanism, while classifying skin lesions. The original network when coupled with Soft-Attention outperforms the baseline[14] by 4.7% while achieving a precision of 93.7% on HAM10000 dataset. Additionally, Soft-Attention coupling improves the sensitivity score by 3.8% compared to baseline[28] and achieves 91.6% on ISIC-2017 dataset. The code is publicly available at github.

scholarly journals Skin Cancer Diseases Classification using Deep Convolutional Neural Network with Transfer Learning Model

2021 ◽  
Vol 2128 (1) ◽  
pp. 012013
Author(s):  
Laila Moataz ◽  
Gouda I. Salama ◽  
Mohamed H. Abd Elazeem
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Classification Accuracy ◽  
Skin Lesions ◽  
Fine Tuning ◽  
Deep Convolutional Neural Networks ◽  
Proposed Model ◽  
Prior Models ◽  
Cancer Diseases

Abstract Skin cancer is becoming increasingly common. Fortunately, early discovery can greatly improve the odds of a patient being healed. Many Artificial Intelligence based approaches to classify skin lesions have recently been proposed. but these approaches suffer from limited classification accuracy. Deep convolutional neural networks show potential for better classification of cancer lesions. This paper presents a fine-tuning on Xception pretrained model for classification of skin lesions by adding a group of layers after the basic ones of the Xception model and all model weights are set to be trained. The model is fine-tuned over HAM10,000 dataset seven classes by augmentation approach to mitigate the data imbalance effect and conducted a comparative study with the most up to date approaches. In comparison to prior models, the results indicate that the proposed model is both efficient and reliable.

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