Deep learning-based image segmentation model using an MRI-based convolutional neural network for physiological evaluation of the heart.

Cardiovascular disease is a high-fatality health issue. Accurate measurement of cardiovascular function depends on precise segmentation of physiological structure and accurate evaluation of functional parameters. Structural segmentation of heart images and calculation of the volume of different ventricular activity cycles form the basis for quantitative analysis of physiological function and can provide the necessary support for clinical physiological diagnosis, as well as the analysis of various cardiac diseases.

Electrocardiogram Signal Classification in the Diagnosis of Heart Disease Based on RBF Neural Network.

Heart disease is a common disease affecting human health. Electrocardiogram (ECG) classification is the most effective and direct method to detect heart disease, which is helpful to the diagnosis of most heart disease symptoms. At present, most ECG diagnosis depends on the personal judgment of medical staff, which leads to heavy burden and low efficiency of medical staff.

Fully connected network with multi-scale dilation convolution module in evaluating atrial septal defect based on MRI segmentation.

Background And Objective: Atrial septal defect (ASD) is a common congenital heart disease. During embryonic development, abnormal atrial septal development leads to pores between the left and right atria. ASD accounts for the largest proportion of congenital heart disease.

Comparative analysis of pulmonary nodules segmentation using multiscale residual U-Net and fuzzy C-means clustering.

Background And Objective: Pulmonary nodules have different shapes and uneven density, and some nodules adhere to blood vessels, pleura and other anatomical structures, which increase the difficulty of nodule segmentation. The purpose of this paper is to use multiscale residual U-Net to accurately segment lung nodules with complex geometric shapes, while comparing it with fuzzy C-means clustering and manual segmentation.

Method: We selected 58 computed tomography (CT) scan images of patients with different lung nodules for image segmentation.

Automatic segmentation of cardiac magnetic resonance images based on multi-input fusion network.

Purpose: Using computer-assisted means to process a large amount of heart image data in order to speed up the diagnosis efficiency and accuracy of medical doctors has become a research worthy of investigation.

Method: Based on the U-Net model, this paper proposes a multi-input fusion network (MIFNet) model based on multi-scale input and feature fusion, which automatically extracts and fuses features of different input scales to realize the detection of Cardiac Magnetic Resonance Images (CMRI). The MIFNet model is trained and verified on the public data set, and then compared with the segmentation models, namely the Fully Convolutional Network (FCN) and DeepLab v1.

Management of medical and health big data based on integrated learning-based health care system: A review and comparative analysis.

Purpose: We present a Health Care System (HCS) based on integrated learning to achieve high-efficiency and high-precision integration of medical and health big data, and compared it with an internet-based integrated system.

Method: The method proposed in this paper adopts the Bagging integrated learning method and the Extreme Learning Machine (ELM) prediction model to obtain a high-precision strong learning model. In order to verify the integration efficiency of the system, we compare it with the Internet-based health big data integration system in terms of integration volume, integration efficiency, and storage space capacity.