[HTML][HTML] A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities
Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent
developments in deep neural networks have contributed to significant advances in medical …
developments in deep neural networks have contributed to significant advances in medical …
Heart rate variability for medical decision support systems: A review
Abstract Heart Rate Variability (HRV) is a good predictor of human health because the heart
rhythm is modulated by a wide range of physiological processes. This statement embodies …
rhythm is modulated by a wide range of physiological processes. This statement embodies …
[HTML][HTML] Detection of pneumonia using convolutional neural networks and deep learning
P Szepesi, L Szilágyi - Biocybernetics and biomedical engineering, 2022 - Elsevier
The objective and automated detection of pneumonia represents a serious challenge in
medical imaging, because the signs of the illness are not obvious in CT or X-ray scans …
medical imaging, because the signs of the illness are not obvious in CT or X-ray scans …
Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques
This study presents a comprehensive systematic review focusing on the applications of deep
learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 …
learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 …
Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression
Big-medical-data classification and image detection are crucial tasks in the field of
healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring …
healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring …
A comprehensive review of model compression techniques in machine learning
This paper critically examines model compression techniques within the machine learning
(ML) domain, emphasizing their role in enhancing model efficiency for deployment in …
(ML) domain, emphasizing their role in enhancing model efficiency for deployment in …
[HTML][HTML] Alexnet architecture variations with transfer learning for classification of wound images
In medical world, wound care and follow-up is one of the issues that are gaining importance
to work on day by day. Accurate and early recognition of wounds can reduce treatment …
to work on day by day. Accurate and early recognition of wounds can reduce treatment …
Enabling cross-type full-knowledge transferable energy management for hybrid electric vehicles via deep transfer reinforcement learning
Deep reinforcement learning (DRL) now represents an emerging artificial intelligence
technology to develop energy management strategies (EMSs) for hybrid electric vehicles …
technology to develop energy management strategies (EMSs) for hybrid electric vehicles …
Current technologies for detection of COVID-19: Biosensors, artificial intelligence and internet of medical things (IOMT)
Despite the fact that COVID-19 is no longer a global pandemic due to development and
integration of different technologies for the diagnosis and treatment of the disease …
integration of different technologies for the diagnosis and treatment of the disease …
An accurate multiple sclerosis detection model based on exemplar multiple parameters local phase quantization: ExMPLPQ
Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the
white matter of the central nervous system that can be detected using magnetic resonance …
white matter of the central nervous system that can be detected using magnetic resonance …