Background and objective Deep learning approaches are being increasingly applied for
medical computer-aided diagnosis (CAD). However, these methods generally target only …

Auroral classification plays a crucial role in polar research. However, current auroral
classification studies are predominantly based on images taken at a single wavelength …

Artificial intelligence algorithms, specifically deep learning, can assist radiologists by
automating mammogram density assessment. However, trust in such algorithms must be …

Breast density classification presents a crucial risk factor for breast cancer. The breast
density evaluation via the breast imaging reporting and data system (BI-RADS) presents …

The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is
traditionally performed by radiologists via visual inspection of mammography images …

Deep learning models have found extensive application in medical imaging analysis,
particularly in mammography classification. However, these models encounter challenges …

Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of
breast cancer. However, most current methods focus primarily on the dual-view analysis of a …

This study introduces a novel framework for enhancing domain generalization in medical
imaging, specifically focusing on utilizing unlabelled multi-view colour fundus photographs …

The status and prospects of signal processing in the field of healthcare. First, some models
of medical imaging and their factors that affect the image quality are intensively investigated …

Alzheimer's disease (AD) is a brain disorder that impairs one's ability to function on a daily
basis. Machine learning (ML) has significantly helped clinicians in quickly and accurately …