Internet of things and deep learning enabled healthcare disease diagnosis using biomedical electrocardiogram signals

Recently, Internet of Things (IoT) and cloud computing environments become commonly employed in several healthcare applications by the integration of monitoring things such as sensors and medical gadgets for observing remote patients. For availing of improved healthcare services, the huge count of data generated by IoT gadgets from the medicinal field can be investigated in the CC environment rather than relying on limited processing and storage resources. At the same time, earlier identification of chronic kidney disease (CKD) becomes essential to reduce the mortality rate significantly. This study develops an ensemble of deep learning based clinical decision support systems (EDL-CDSS) for CKD diagnosis in the IoT environment. The goal of the EDL-CDSS technique is to detect and classify different stages of CKD using the medical data collected by IoT devices and benchmark repositories. In addition, the EDL-CDSS technique involves the design of Adaptive Synthetic (ADASYN) technique for outlier detection process. Moreover, an ensemble of three models, namely, deep belief network (DBN), kernel extreme learning machine (KELM), and convolutional neural network with gated recurrent unit (CNN-GRU), are performed. Finally, quasi-oppositional butterfly optimization algorithm (QOBOA) is used for the hyperparameter tuning of the DBN and CNN-GRU models. A wide range of simulations was carried out and the outcomes are studied in terms of distinct measures. A brief outcomes analysis highlighted the supremacy of the EDL-CDSS technique on exiting approaches.

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Deep Learning in Disease Diagnosis: Models and Datasets

Current Bioinformatics ◽

10.2174/1574893615999201002124021 ◽

2020 ◽

Vol 15 ◽

Author(s):

Deeksha Saxena ◽

Mohammed Haris Siddiqui ◽

Rajnish Kumar

Keyword(s):

Biological Sciences ◽

Machine Learning ◽

Deep Learning ◽

Disease Diagnosis ◽

Learning Models ◽

Data Types ◽

Related Data ◽

Abstract Level ◽

Experimental Validations ◽

Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

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Internet of Things-enabled Sustainability, Industrial Big Data Analytics, and Deep Learning-assisted Smart Process Planning in Cyber-Physical Manufacturing Systems

Economics Management and Financial Markets ◽

10.22381/emfm15420205 ◽

2020 ◽

Vol 15 (4) ◽

pp. 49

Keyword(s):

Big Data ◽

Deep Learning ◽

Internet Of Things ◽

Process Planning ◽

Data Analytics ◽

Manufacturing Systems ◽

Big Data Analytics ◽

Industrial Big Data

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DLIFT: A deep‐learning‐based intelligent fund transaction system for financial Internet of Things

Concurrency and Computation Practice and Experience ◽

10.1002/cpe.5982 ◽

2020 ◽

Author(s):

Gang Hu ◽

Zhuoyuan Xiang ◽

Yin Zhang ◽

M. Shamim Hossain

Keyword(s):

Deep Learning ◽

Internet Of Things

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The robust deep learning–based schemes for intrusion detection in Internet of Things environments

annals of telecommunications - annales des télécommunications ◽

10.1007/s12243-021-00854-y ◽

2021 ◽

Author(s):

Xingbing Fu ◽

Nan Zhou ◽

Libin Jiao ◽

Haifeng Li ◽

Jianwu Zhang

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Intrusion Detection

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Deep learning for intelligent diagnosis in thyroid scintigraphy

Journal of International Medical Research ◽

10.1177/0300060520982842 ◽

2021 ◽

Vol 49 (1) ◽

pp. 030006052098284

Author(s):

Tingting Qiao ◽

Simin Liu ◽

Zhijun Cui ◽

Xiaqing Yu ◽

Haidong Cai ◽

...

Keyword(s):

Deep Learning ◽

Disease Diagnosis ◽

Kappa Coefficient ◽

Diagnostic Assessment ◽

First Year ◽

Thyroid Scintigraphy ◽

Diagnostic Ability ◽

The Third ◽

Classification Time ◽

Better Than

Objective To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy. Methods We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model’s performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents. Results The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided “diagnostic assistance” to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents. Conclusion DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves’ disease and subacute thyroiditis.

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