The state of quantum computing applications in health and medicine
FF Flöther - Research Directions: Quantum Technologies, 2023 - cambridge.org
Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-
related activities and experiments in the last few years (although biology and quantum …
related activities and experiments in the last few years (although biology and quantum …
Programming quantum neural networks on NISQ systems: an overview of technologies and methodologies
S Markidis - Entropy, 2023 - mdpi.com
Noisy Intermediate-Scale Quantum (NISQ) systems and associated programming interfaces
make it possible to explore and investigate the design and development of quantum …
make it possible to explore and investigate the design and development of quantum …
Accessing artificial intelligence for fetus health status using hybrid deep learning algorithm (AlexNet-SVM) on cardiotocographic data
Artificial intelligence is serving as an impetus in digital health, clinical support, and health
informatics for an informed patient's outcome. Previous studies only consider classification …
informatics for an informed patient's outcome. Previous studies only consider classification …
Early-stage Alzheimer's disease categorization using PET neuroimaging modality and convolutional neural networks in the 2D and 3D domains
Alzheimer's Disease (AD) is a health apprehension of significant proportions that is
negatively impacting the ageing population globally. It is characterized by neuronal loss and …
negatively impacting the ageing population globally. It is characterized by neuronal loss and …
Implementing magnetic resonance imaging brain disorder classification via AlexNet–quantum learning
The classical neural network has provided remarkable results to diagnose neurological
disorders against neuroimaging data. However, in terms of efficient and accurate …
disorders against neuroimaging data. However, in terms of efficient and accurate …
Unlocking the potential of quantum machine learning to advance drug discovery
The drug discovery process is a rigorous and time-consuming endeavor, typically requiring
several years of extensive research and development. Although classical machine learning …
several years of extensive research and development. Although classical machine learning …
Deep learning paradigm for cardiovascular disease/stroke risk stratification in Parkinson's disease affected by COVID-19: a narrative review
Background and Motivation: Parkinson's disease (PD) is one of the most serious, non-
curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to …
curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to …
Barnacles mating optimizer with deep transfer learning enabled biomedical malaria parasite detection and classification
Biomedical engineering involves ideologies and problem‐solving methods of engineering to
biology and medicine. Malaria is a life‐threatening illness, which has gained significant …
biology and medicine. Malaria is a life‐threatening illness, which has gained significant …
Hybrid classical–quantum transfer learning for cardiomegaly detection in chest x-rays
Cardiovascular diseases are among the major health problems that are likely to benefit from
promising developments in quantum machine learning for medical imaging. The chest X-ray …
promising developments in quantum machine learning for medical imaging. The chest X-ray …
[PDF][PDF] A Deep Learning for Alzheimer's Stages Detection Using Brain Images.
Alzheimer's disease (AD) is a chronic and common form of dementia that mainly affects
elderly individuals. The disease is dangerous because it causes damage to brain cells and …
elderly individuals. The disease is dangerous because it causes damage to brain cells and …