Biomedical data are becoming increasingly multimodal and thereby capture the underlying
complex relationships among biological processes. Deep learning (DL)-based data fusion …

Sudden spurting of Corona virus disease (COVID-19) has put the whole healthcare system
on high alert. Internet of Medical Things (IoMT) has eased the situation to a great extent, also …

Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single
data modality to make predictions such as AD stages. The fusion of multiple data modalities …

The Internet of Things (IoT) is a system of wireless, interrelated, and connected digital
devices that can collect, send, and store data over a network without requiring human-to …

With the wide deployments of heterogeneous networks, huge amounts of data with
characteristics of high volume, high variety, high velocity, and high veracity are generated …

Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is
practised. AI platforms excel in recognizing complex patterns in medical data and provide a …

Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …

Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

Accurate and precise brain tumor MR images classification plays important role in clinical
diagnosis and decision making for patient treatment. The key challenge in MR images …

Abstract Machine learning, particularly deep learning has boosted medical image analysis
over the past years. Training a good model based on deep learning requires large amount …