Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography
(ECG) still represents the benchmark approach for identifying cardiac irregularities …

The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …

The deep learning models such as AlexNet, VGG, and ResNet achieved a good
performance in classifying the breast cancer histopathological images in BreakHis dataset …

Magnification-independent (MI) classification is considered a promising method for detecting
the histopathological images of breast cancer. However, it has too many parameters for real …

Nowadays, the number of sudden deaths due to heart disease is increasing with the
coronavirus pandemic. Therefore, automatic classification of electrocardiogram (ECG) …

According to the recent studies on feature-based knowledge distillation (KD), a student
model will not be able to imitate a teacher's behavior properly if there is a high variance …

Single-lead wearable electrocardiographic (ECG) devices for remote monitoring are
emerging as critical components of the viability of long-term continuous health and wellness …

Mobile health devices with automatic electrocardiogram diagnosis models facilitate long-
term cardiac monitoring and enhance the sensitivity of detecting paroxysmal cardiovascular …

The 12 leads of electrocardiogram (ECG) signals show the heart activities from different
angles of coronal and axial planes; hence, the signals of these 12 leads have functional …

Electrocardiogram (ECG) is a widely used technique for diagnosing cardiovascular disease.
The widespread emergence of smart ECG devices has sparked the demand for intelligent …