A comprehensive survey on multimodal medical signals fusion for smart healthcare systems
Smart healthcare is a framework that utilizes technologies such as wearable devices, the
Internet of Medical Things (IoMT), sophisticated machine learning algorithms, and wireless …
Internet of Medical Things (IoMT), sophisticated machine learning algorithms, and wireless …
Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects
Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues
Abstract Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …
Medical image-based detection of COVID-19 using Deep Convolution Neural Networks
The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across
the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of …
the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of …
Automated detection and forecasting of covid-19 using deep learning techniques: A review
Abstract In March 2020, the World Health Organization (WHO) declared COVID-19 a global
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
[HTML][HTML] A smart ontology-based IoT framework for remote patient monitoring
Abstract The Internet of Things (IoT) is the most promising technology in health technology
systems. IoT-based systems ensure continuous monitoring in indoor and outdoor settings …
systems. IoT-based systems ensure continuous monitoring in indoor and outdoor settings …
Electroencephalography-based motor imagery classification using temporal convolutional network fusion
YK Musallam, NI AlFassam, G Muhammad… - … Signal Processing and …, 2021 - Elsevier
Motor imagery electroencephalography (MI-EEG) signals are generated when a person
imagines a task without actually performing it. In recent studies, MI-EEG has been used in …
imagines a task without actually performing it. In recent studies, MI-EEG has been used in …
Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence
SI Nafisah, G Muhammad - Neural Computing and Applications, 2024 - Springer
In most regions of the world, tuberculosis (TB) is classified as a malignant infectious disease
that can be fatal. Using advanced tools and technology, automatic analysis and …
that can be fatal. Using advanced tools and technology, automatic analysis and …
Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification
Abstract The Covid-19 European outbreak in February 2020 has challenged the world's
health systems, eliciting an urgent need for effective and highly reliable diagnostic …
health systems, eliciting an urgent need for effective and highly reliable diagnostic …
A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research
The combination of class imbalance and overlap is currently one of the most challenging
issues in machine learning. While seminal work focused on establishing class overlap as a …
issues in machine learning. While seminal work focused on establishing class overlap as a …