Survival study on deep learning techniques for IoT enabled smart healthcare system
AK Munnangi, S UdhayaKumar, V Ravi… - Health and …, 2023 - Springer
Purpose The paper is to study a review of the employment of deep learning (DL) techniques
inside the healthcare sector, together with the highlight of the strength and shortcomings of …
inside the healthcare sector, together with the highlight of the strength and shortcomings of …
ANFIS-Net for automatic detection of COVID-19
Among the most leading causes of mortality across the globe are infectious diseases which
have cost tremendous lives with the latest being coronavirus (COVID-19) that has become …
have cost tremendous lives with the latest being coronavirus (COVID-19) that has become …
Adoption of telehealth technologies: an approach to improving healthcare system
Background Globally, the healthcare industry is well known to be one of the strongest drivers
of economic growth and development. The sector has gained substantial attention to deal …
of economic growth and development. The sector has gained substantial attention to deal …
iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients
Most prior studies focused on developing models for the severity or mortality prediction of
COVID-19 patients. However, effective models for recovery-time prediction are still lacking …
COVID-19 patients. However, effective models for recovery-time prediction are still lacking …
Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation
A Reshetnikov, V Berdutin, A Zaporozhtsev… - BMC Medical Informatics …, 2023 - Springer
Background Outbreaks of infectious diseases are a complex phenomenon with many
interacting factors. Regional health authorities need prognostic modeling of the epidemic …
interacting factors. Regional health authorities need prognostic modeling of the epidemic …
Computer-aided diagnosis of COVID-19 from chest X-ray images using histogram-oriented gradient features and Random Forest classifier
The decision-making process is very crucial in healthcare, which includes quick diagnostic
methods to monitor and prevent the COVID-19 pandemic disease from spreading …
methods to monitor and prevent the COVID-19 pandemic disease from spreading …
[PDF][PDF] Deep Learning Applications for COVID-19 Analysis: A State-of-the-Art Survey.
W Li, X Deng, H Shao, X Wang - CMES-Computer Modeling in …, 2021 - cdn.techscience.cn
The COVID-19 has resulted in catastrophic situation and the deaths of millions of people all
over the world. In this paper, the predictions of epidemiological propagation models, such as …
over the world. In this paper, the predictions of epidemiological propagation models, such as …
Detection of COVID-19 Using Deep Learning Techniques and Cost Effectiveness Evaluation: A Survey.
MV Mk, S Atalla, N Almuraqab… - Frontiers in Artificial …, 2022 - europepmc.org
Graphical-design-based symptomatic techniques in pandemics perform a quintessential
purpose in screening hit causes that comparatively render better outcomes amongst the …
purpose in screening hit causes that comparatively render better outcomes amongst the …
A deep learning-driven low-power, accurate, and portable platform for rapid detection of COVID-19 using reverse-transcription loop-mediated isothermal amplification
This paper presents a deep learning-driven portable, accurate, low-cost, and easy-to-use
device to perform Reverse-Transcription Loop-Mediated Isothermal Amplification (RT …
device to perform Reverse-Transcription Loop-Mediated Isothermal Amplification (RT …
Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images
W Wang, Y Jiang, X Wang, P Zhang, J Li - BMC Medical Imaging, 2022 - Springer
Abstract Background Corona Virus Disease 2019 (COVID-19) first appeared in December
2019, and spread rapidly around the world. COVID-19 is a pneumonia caused by novel …
2019, and spread rapidly around the world. COVID-19 is a pneumonia caused by novel …