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The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things
S Aminizadeh, A Heidari, S Toumaj, M Darbandi… - Computer methods and …, 2023 - Elsevier
Medical data processing has grown into a prominent topic in the latest decades with the
primary goal of maintaining patient data via new information technologies, including the …
primary goal of maintaining patient data via new information technologies, including the …
[HTML][HTML] IoT for smart cities: Machine learning approaches in smart healthcare—A review
Smart city is a collective term for technologies and concepts that are directed toward making
cities efficient, technologically more advanced, greener and more socially inclusive. These …
cities efficient, technologically more advanced, greener and more socially inclusive. These …
Brain tumor detection based on deep learning approaches and magnetic resonance imaging
Simple Summary In this research, we addressed the challenging task of brain tumor
detection in MRI scans using a large collection of brain tumor images. We demonstrated that …
detection in MRI scans using a large collection of brain tumor images. We demonstrated that …
Transfer learning-motivated intelligent fault diagnosis designs: A survey, insights, and perspectives
Over the last decade, transfer learning has attracted a great deal of attention as a new
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …
Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions
In recent times, the machine learning (ML) community has recognized the deep learning
(DL) computing model as the Gold Standard. DL has gradually become the most widely …
(DL) computing model as the Gold Standard. DL has gradually become the most widely …
A robust approach for brain tumor detection in magnetic resonance images using finetuned efficientnet
A brain tumor is a disorder caused by the growth of abnormal brain cells. The survival rate of
a patient affected with a tumor is difficult to determine because they are infrequent and …
a patient affected with a tumor is difficult to determine because they are infrequent and …
Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework
E Irmak - Iranian Journal of Science and Technology …, 2021 - Springer
Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy
specimens today. The current method is invasive, time-consuming and prone to manual …
specimens today. The current method is invasive, time-consuming and prone to manual …
Deep learning for safe autonomous driving: Current challenges and future directions
Advances in information and signal processing technologies have a significant impact on
autonomous driving (AD), improving driving safety while minimizing the efforts of human …
autonomous driving (AD), improving driving safety while minimizing the efforts of human …
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
[HTML][HTML] Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
Medical artificial intelligence (AI) systems have been remarkably successful, even
outperforming human performance at certain tasks. There is no doubt that AI is important to …
outperforming human performance at certain tasks. There is no doubt that AI is important to …