Biomedical image classification is crucial for both computer vision tasks and clinical care.
The conventional method requires a significant amount of time and effort for extracting and …

Quantum machine learning is emerging as a strategy to solve real‐world problems. As a
quantum computing model, parameterized quantum circuits provide an approach for …

Background In medical diagnosis and clinical practice, diagnosing a disease early is crucial
for accurate treatment, lessening the stress on the healthcare system. In medical imaging …

Image classification plays an important role in remote sensing. Earth observation (EO) has
inevitably arrived in the big data era, but the high requirement on computation power has …

Background COVID-19 is a novel virus that affects the upper respiratory tract, as well as the
lungs. The scale of the global COVID-19 pandemic, its spreading rate, and deaths are …

Deep learning achieves unprecedented success involves many fields, whereas the high
requirement of memory and time efficiency tolerance have been the intractable challenges …

Convolutional neural networks (CNNs) have rapidly risen in popularity for many machine
learning applications, particularly in the field of image recognition. Much of the benefit …

Image classification is a major application domain for conventional deep learning (DL).
Quantum machine learning (QML) has the potential to revolutionize image classification. In …

Abstract The Internet of Medical Things (IoMT) has emerged as a significant research area in
the medical field, enabling the transmission of various types of data to the cloud for analysis …

Quantum technologies have become powerful tools for a wide range of application
disciplines, which tend to range from chemistry to agriculture, natural language processing …