Multi-Disease Prediction Based on Deep Learning: A Survey

Xie, Shuxuan; Yu, Zengchen; Lv, Zhihan

doi:10.32604/cmes.2021.016728

Cited by 154 publications

(112 citation statements)

References 166 publications

(171 reference statements)

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“…About 40% of sepsis patients suffer from myocardial damage. Therefore, the examination and diagnosis of early cardiac function of patients are a key method commonly used in clinical practice [ 14 ]. The reconstruction process of ultrasound image transmission and calculation is affected by various objective factors, such as the reduction of image resolution, which brings difficulties to the diagnosis and judgment of doctors in medical image processing.…”

Section: Discussionmentioning

confidence: 99%

E-Health Ultrasonic Diagnostic Monitoring for Analysis of Cardiac Insufficiency and Neuronal Regulation in Patients with Sepsis in Emergency Department under Image Reconstruction Algorithm

Fang

Mao

2022

Computational and Mathematical Methods in Medicine

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An anisotropic diffusion filtering- (ADF-) ultrasound (ADF-U) for ultrasound reconstruction was constructed based on the ADF to explore the diagnostic application of ultrasound imaging based on electronic health (E-health) for cardiac insufficiency and neuronal regulation in patients with sepsis. The 144 patients with sepsis were divided into an experimental group (78 patients with cardiac insufficiency) and a control group (66 patients with normal cardiac function), and another 58 healthy people were included in a blank control. The ultrasound examination was performed on all patients. In addition, new ultrasound image reconstruction and diagnosis were performed based on ADF and E-health, and its reconstruction effects were compared with those of the Bilateral Filter-ultrasonic (BFU) algorithm and the Wavelet Threshold-ultrasonic (WTU) algorithm. The left and right ventricular parameters and neuropeptide levels were detected and recorded. The results show that the running time, average gradient (AG), and peak signal-to-noise ratio (SNR) (PSNR) of the ADF-U algorithm were greater than those of the Bilateral Filter-ultrasonic (BFU) and Wavelet Threshold-ultrasonic (WTU), but the mean square error (MSE) was opposite ( P < 0.05 ); the left ventricular end-systolic volume (LVESV) and the vertical distance between the mitral valve E-point to septal separation (EPSS) in the experimental group were higher than those in the control and blank group, while the left ventricular ejection fraction (LVEF), stroke volume (SV), cardiac output (CO), and left ventricular fractional shortening (LVFS) were opposite ( P < 0.05 ); the systolic peak velocity of right ventricular free wall tricuspid annulus (Sm) and pulmonary valve blood velocity (PVBV) in the experimental group were lower than those of the control group and blank group ( P < 0.05 ); the messenger ribonucleic acid (mRNA) of Proopiomelanocortin (POMC) and Cocain and amphetamine-regulated transcript (CART) was higher than the mRNA IN control group and blank group ( P < 0.05 ). In short, the ADF-U algorithm proposed in this study improved the resolution, SNR, and reconstruction efficiency of E-health ultrasound images and provided an effective reference value for the diagnosis of cardiac insufficiency and neuronal adjustment analysis in patients with sepsis in the emergency department.

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Section: Discussionmentioning

confidence: 99%

E-Health Ultrasonic Diagnostic Monitoring for Analysis of Cardiac Insufficiency and Neuronal Regulation in Patients with Sepsis in Emergency Department under Image Reconstruction Algorithm

Fang

Mao

2022

Computational and Mathematical Methods in Medicine

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“…The CNN model was used to determine whether the image block was a cell. This method can automatically detect cells [ 26 ]. The intracellular division was in this module.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Technology in Pathological Image Analysis of Breast Tissue

Liu

Wang

et al. 2021

Journal of Healthcare Engineering

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To explore the application value of the multilevel pyramid convolutional neural network (MPCNN) model based on convolutional neural network (CNN) in breast histopathology image analysis, in this study, based on CNN algorithm and softmax classifier (SMC), a sparse autoencoder (SAE) is introduced to optimize it. The sliding window method is used to identify cells, and the CNN + SMC pathological image cell detection method is established. Furthermore, the local region active contour (LRAC) is introduced to optimize it and the LRAC fine segmentation model driven by local Gaussian distribution is established. On this basis, the sparse automatic encoder is further introduced to optimize it and the MPCNN model is established. The proposed algorithm is evaluated on the pathological image data set. The results showed that the Acc value, F value, and Re value of pathological cell detection of CNN + SMC algorithm were significantly higher than those of the other two algorithms ( P < 0.05). The Dice, OL, Sen, and Spe values of pathological image regional segmentation of CNN algorithm were significantly higher than those of the other two algorithms, and the difference was statistically significant ( P < 0.05). The accuracy, recall, and F-measure of the optimized CNN algorithm for detecting breast histopathological images were 85.25%, 89.27%, and 80.09%, respectively. In the two databases with segmentation standards, the segmentation accuracy of MPCNN is 55%, 73.1%, 78.8%, and 82.1%. In the deep convolution network model, the training time of the MPCNN algorithm is about 80 min. It shows that when the feature dimension is low, the feature map extracted by MPCNN is more effective than the traditional feature extraction method.

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“…This will play an important role in improving medical care and providing treatment options. It plays an important role in promoting clinical practice and decision-making [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

The Relationship between the Mechanism of Sarcopenia and Exercise Based on Data Mining

Zheng

Li³

2022

Computational and Mathematical Methods in Medicine

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Due to the increasing prosperity of human life science and technology, many huge research results have been obtained, and the scientific research of molecular biology is developing rapidly. Therefore, the output of biological genome data has increased exponentially, which constitutes a huge amount of data analysis. The seemingly chaotic and massive amount of data information actually contains a large amount of data and information of great key scientific significance and value. Therefore, this kind of genomic data information not only contains the information content that describes the characteristics of human life but also contains the information content that can express the essence of the biological organism. It includes macroeconomic information that can reflect the basic structure and capabilities of living organisms and microinformation in related fields of molecular biology. This massive amount of genetic data is usually closely related to each other, can influence each other, and does not exist alone. In the article, the causes of uncertain data and the classification of uncertain data are introduced, and the basic concepts and related algorithms of data mining are explained. Focusing on the research and analysis of abnormal point detection and clustering algorithms in uncertain data mining technology, this paper solves the problem of how to obtain more diverse and accurate outlier detection and cluster analysis results in uncertain data. The results showed that whether it was related to obesity or not, the Lp(a) level of the sarcopenia group was significantly higher than that of the nonsarcopenia group. At the same time, the correlation analysis showed that ASM/height was negatively correlated with Lp(a). ASM/height is one of the criteria for diagnosing sarcoidosis, and it is also the core of the analysis. Among the 1956 tumor patients collected in this study, 432 had sarcopenia, accounting for 22.08%, and the incidence of sarcopenia in patients with gastrointestinal tumors increased.

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Multi-Disease Prediction Based on Deep Learning: A Survey

Cited by 154 publications

References 166 publications

E-Health Ultrasonic Diagnostic Monitoring for Analysis of Cardiac Insufficiency and Neuronal Regulation in Patients with Sepsis in Emergency Department under Image Reconstruction Algorithm

E-Health Ultrasonic Diagnostic Monitoring for Analysis of Cardiac Insufficiency and Neuronal Regulation in Patients with Sepsis in Emergency Department under Image Reconstruction Algorithm

Deep Learning Technology in Pathological Image Analysis of Breast Tissue

The Relationship between the Mechanism of Sarcopenia and Exercise Based on Data Mining

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