COVID-19 has crippled the world's healthcare systems, setting back the economy and taking
the lives of several people. Although potential vaccines are being tested and supplied …

Despite its undeniable success, classical machine learning remains a resource-intensive
process. Practical computational efforts for training state-of-the-art models can now only be …

Recently, the amount of attention paid towards convolutional neural networks (CNN) in
medical image analysis has rapidly increased since they can analyze and classify images …

As image processing techniques constantly grow in complexity & volume, meeting the
required demand for data storage and computational power is a challenge. Using hybrid …

Image classification, which is a fundamental element of Computer Vision (CV) and Artificial
Intelligence (AI), has been researched intensively in numerous domains and embedded in …

Quantum machine learning recently gained prominence due to the computational ability of
quantum computers in solving machine learning problems that are intractable on a classical …

Multi-modal medical image fusion is a challenging yet important task for precision diagnosis
and surgical planning in clinical practice. Although single feature fusion strategy such as …

Diagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19)
outbreak which is declared as a pandemic by the world health organization (WHO) is …

This paper presents an in-depth study and exploration of the health IoT architecture and
related implementation technologies from both theoretical and practical aspects, with …

Healthcare data are inherently multimodal, including electronic health records (EHR),
medical images, and multi-omics data. Combining these multimodal data sources …