Detection of Atrial Fibrillation based on Feature Fusion using Attention-based BiLSTM.

Atrial fibrillation (AF) is a common cardiac arrhythmia, and its early detection is crucial for timely treatment. Conventional methods, such as Electrocardiogram (ECG), can be intrusive and require specialized equipment, whereas Photoplethysmography (PPG) offers a non-invasive alternative. In this study, we present a feature fusion approach for AF detection using attention-based Bidirectional Long Short-Term Memory (BiLSTM) and PPG signals.

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.

Multi-domain medical image translation generation for lung image classification based on generative adversarial networks.

Objective: Lung image classification-assisted diagnosis has a large application market. Aiming at the problems of poor attention to existing translation models, the insufficient ability of key transfer and generation, insufficient quality of generated images, and lack of detailed features, this paper conducts research on lung medical image translation and lung image classification based on generative adversarial networks.

Methods: This paper proposes a medical image multi-domain translation algorithm MI-GAN based on the key migration branch.

DE-MRI myocardial fibrosis segmentation and classification model based on multi-scale self-supervision and transformer.

Objective: The segmentation and categorization of fibrotic tissue in time-lapse enhanced MRI scanning are quite challenging, and it is mainly done manually for myocardial DE-MRI images. On the other hand, DE-MRI instructions for segmenting and classifying cardiac hypertrophy are complex and prone to inaccuracy. Developing cardiac DE-MRI classification and prediction methods is crucial.

Unsupervised domain adaptive myocardial infarction MRI classification diagnostics model based on target domain confused sample resampling.

Objective: Inefficient circulatory system due to blockage of blood vessels leads to myocardial infarction and acute blockage. Myocardial infarction is frequently classified and diagnosed in medical treatment using MRI, yet this method is ineffective and prone to error. As a result, there are several implementation scenarios and clinical significance for employing deep learning to develop computer-aided algorithms to aid cardiologists in the routine examination of cardiac MRI.

Classification of myocardial fibrosis in DE-MRI based on semi-supervised semantic segmentation and dual attention mechanism.

Objective: It is essential to utilize cardiac delayed-enhanced magnetic resonance imaging (DE-MRI) to diagnose cardiovascular disease. By segmenting myocardium DE-MRI images, it provides critical information for the evaluation and treatment of myocardial infarction. As a consequence, it is vital to investigate the segmentation and classification technique of myocardial DE-MRI.

Classification of lungs infected COVID-19 images based on inception-ResNet.

Objective: Nowadays, COVID-19 is spreading rapidly worldwide, and seriously threatening lives . From the perspective of security and economy, the effective control of COVID-19 has a profound impact on the entire society. An effective strategy is to diagnose earlier to prevent the spread of the disease and prompt treatment of severe cases to improve the chance of survival.