Deep Learning in Smart Health: Methodologies, Applications, Challenges

2019 ◽  
pp. 23-46
Author(s):  
Murat Simsek ◽  
Alex Adim Obinikpo ◽  
Burak Kantarci
Keyword(s):  
Deep Learning ◽  
Smart Health

scholarly journals Systematic review of smart health monitoring using deep learning and Artificial intelligence

2021 ◽  
pp. 100028
Author(s):  
A.V.L.N. Sujith ◽  
Guna Sekhar Sajja ◽  
V. Mahalakshmi ◽  
Shibili Nuhmani ◽  
B. Prasanalakshmi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Deep Learning ◽  
Health Monitoring ◽  
Smart Health

Synergic Deep Learning for Smart Health Diagnosis of COVID-19 for Connected Living and Smart Cities

2022 ◽  
Vol 22 (3) ◽  
pp. 1-14
Author(s):  
K. Shankar ◽  
Eswaran Perumal ◽  
Mohamed Elhoseny ◽  
Fatma Taher ◽  
B. B. Gupta ◽  
...  
Keyword(s):  
Deep Learning ◽  
Smart Cities ◽  
Imaging Techniques ◽  
Input Image ◽  
Significant Loss ◽  
Deep Convolutional Neural Networks ◽  
Smart Health ◽  
And Control ◽  
Health Diagnosis

COVID-19 pandemic has led to a significant loss of global deaths, economical status, and so on. To prevent and control COVID-19, a range of smart, complex, spatially heterogeneous, control solutions, and strategies have been conducted. Earlier classification of 2019 novel coronavirus disease (COVID-19) is needed to cure and control the disease. It results in a requirement of secondary diagnosis models, since no precise automated toolkits exist. The latest finding attained using radiological imaging techniques highlighted that the images hold noticeable details regarding the COVID-19 virus. The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional neural networks (DCNNs) and involves a mutual learning process from one another. Particularly, the representation of images learned by both DCNNs is provided as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images come under the identical class. Besides, the proposed SDL model involves a fuzzy bilateral filtering (FBF) model to pre-process the input image. The integration of FBL and SDL resulted in the effective classification of COVID-19. To investigate the classifier outcome of the SDL model, a detailed set of simulations takes place and ensures the effective performance of the FBF-SDL model over the compared methods.

Smart Healthcare for ECG Telemonitoring System

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Jwan Najeeb Saeed ◽  
◽  
Siddeeq Y. Ameen ◽  
Keyword(s):  
Health Care ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Monitoring ◽  
Feature Learning ◽  
Human Activity Recognition ◽  
Smart Healthcare ◽  
Learning Techniques ◽  
Smart Health

Cardiovascular disorders are one of the major causes of sad death among older and middle-aged people. Over the past two decades, health monitoring services have evolved quickly and had the ability to change the way health care is currently provided. However, the most challenging aspect of the mobile and wearable sensor-based human activity recognition pipeline is the extraction of the related features. Feature extraction decreases both computational complexity and time. Deep learning techniques are used for automatic feature learning in a variety of fields, including health, image classification, and, most recently, for the extraction and classification of complex and straightforward human activity recognition in smart health care. This paper reviews the recent state of the art in electrocardiogram (ECG) smart health monitoring systems based on the Internet of things with the machine and deep learning techniques. Moreover, the paper provided possible research and challenges that can help researchers advance state of art in future work.

Prediction of heart abnormalities using deep learning model and wearabledevices in smart health homes

2021 ◽  
Author(s):  
Jana Shafi ◽  
Mohammad S. Obaidat ◽  
P. Venkata Krishna ◽  
Balqies Sadoun ◽  
M. Pounambal ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Heart Abnormalities ◽  
Smart Health ◽  
Health Homes ◽  
Deep Learning Model

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Vollautomatische Quantifizierung der Volumina und Funktion des linken Ventrikels in der kardialen MRT: Evaluation eines Deep-Learning basierten Algorithmus

2020 ◽  
Author(s):  
B Böttcher ◽  
E Beller ◽  
A Busse ◽  
F Streckenbach ◽  
M Weber ◽  
...  
Keyword(s):  
Deep Learning
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