In the dynamic field of medical artificial intelligence (AI), cardiology stands out as a key area
for its technological advancements and clinical application. This review explores the …

In social IoMT systems, resource-constrained devices face the challenges of limited
computation, bandwidth, and privacy in the deployment of deep learning models. Federated …

Most devices in the Internet of Things (IoT) work on unsafe networks and are constrained by
limited computing, power, and storage resources. Since the existing centralized signature …

Cardiovascular diseases (CVD) are a leading cause of death globally, and result in
significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial …

Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of
being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a …

The rapid rise of healthcare social media websites captures a significant amount of
healthcare information, leading to mining medical content for pharmacovigilance …

Medical data often exhibit class imbalance, which poses a challenge in classification tasks.
To solve this problem, data augmentation techniques are used to balance the data …

Detecting lung pathologies is critical for precise medical diagnosis. In the realm of diagnostic
methods, various approaches, including imaging tests, physical examinations, and …

Myocardial scar (MS) and left ventricular ejection fraction (LVEF) are vital cardiovascular
parameters, conventionally determined using cardiac magnetic resonance (CMR). However …

To achieve optimal performance in practical applications, the electrocardiogram (ECG)
diagnosis models have to be personalized using the ECG data of specific patients. Most …