Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems

Toor, Affan Ahmed; Usman, Muhammad; Younas, Farah; Fong, A.C.M.; Khan, Sajid Ali; Fong, Simon

doi:10.3390/s20072131

Cited by 35 publications

(13 citation statements)

References 27 publications

(58 reference statements)

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“…T1-weighted and T2-weighted images are most commonly used for highlighting the anatomy and lesions, respectively. Faced with diverse MRI images, doctors often need specialized software to read and understand images, such as Picture Archiving and Communication Systems (PACS) as the large amount information [54] provided by the recorded images which the clinician cannot obtain by the naked eyes directly. As an auxiliary diagnostic tool, the MRI images are characterized by stressing body tissue information, weakening boundary information sometimes.…”

Section: Introductionmentioning

confidence: 99%

MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

Zhu

Zhang

et al. 2020

Multimed Tools Appl

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Motivation: Medical image enhancement is a crucial part to improve the quality of the images. The excellent visual effects and image quality can help doctors make quick diagnoses. Among medical images, Magnetic Resonance Imaging (MRI) images play a vital role in clinical diagnosis. Its imaging principle highlights the human tissue part ignoring the boundary information sometimes. Moreover, some imaging results lose details in visual due to the low contrast and the quality of the images. To overcome these limitations, we propose an MRI enhancement method based on visual-attention by means of contrast adjustment and illumination component preservation. Description: The proposed framework includes image generation and image fusion to tackle the limitation of a single image. First, we assume an MRI image composed of tissues and details. We design an adaptive attenuation weight matrix based on the input MRI image according to a new definition of pixel energy. Then, an illumination-preserving image is introduced into the model for the attenuated image as compensation. Finally, an effective image fusion decision map calculation method is devised to create an enhanced MRI image with higher contrast and better perceptual quality. Results and conclusion: The experimental results show that it is a more effective enhancement method which has better performance on most of the objective evaluation metrics and stability than other 14 methods as well as maintains the balance between contrast and illumination of enhanced MRI images.

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Section: Introductionmentioning

confidence: 99%

MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

Zhu

Zhang

et al. 2020

Multimed Tools Appl

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“…Once the training module completes execution, the old inference module at the Fog layer is replaced with the latest one. This approach resists the effect of concept drifting and enable the model to cope with the change in the distribution of the data over time [28]. Thus, the proposed system always retains an acceptable rate in case of prediction as time passes.…”

Section: System Architecturementioning

confidence: 98%

One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments

et al. 2021

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Cardiovascular diseases are considered the number one cause of death across the globe which can be primarily identified by the abnormal heart rhythms of the patients. By generating electrocardiogram (ECG) signals, wearable Internet of Things (IoT) devices can consistently track the patient's heart rhythms. Although Cloud-based approaches for ECG analysis can achieve some levels of accuracy, they still have some limitations, such as high latency. Conversely, the Fog computing infrastructure is more powerful than edge devices but less capable than Cloud computing for executing compositionally intensive data analytic software. The Fog infrastructure can consist of Fog-based gateways directly connected with the wearable devices to offer many advanced benefits, including low latency and high quality of services. To address these issues, a modular one-dimensional convolution neural network (1D-CNN) approach is proposed in this work. The inference module of the proposed approach is deployable over the Fog infrastructure for analysing the ECG signals and initiating the emergency countermeasures within a minimum delay, whereas its training module is executable on the computationally enriched Cloud data centers. The proposed approach achieves the F1-measure score ≈1 on the MIT-BIH Arrhythmia database when applying GridSearch algorithm with the cross-validation method. This approach has also been implemented on a single-board computer and Google Colab-based hybrid Fog-Cloud infrastructure and embodied to a remote patient monitoring system that shows 25% improvement in the overall response time.

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“…Realizing applications such as CAVs and tele-surgery would require ultra-reliable and extremely-low latency connectivity beyond the capabilities of 5G [341]. Moreover, these applications will require powerful edge intelligence capabilities to process data in near real-time [342]. for instance, holographic communication requires the processing and transferring of large number of pixels in real-time to provide a ultimate user experience [30].…”

Section: A Ultimate Mobile Experiencementioning

confidence: 99%

Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research

Alwis

Kalla

Pham

et al. 2021

IEEE Open J. Commun. Soc.

446

195

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Emerging applications such as Internet of Everything, Holographic Telepresence, collaborative robots, and space and deep-sea tourism are already highlighting the limitations of existing fifth-generation (5G) mobile networks. These limitations are in terms of data-rate, latency, reliability, availability, processing, connection density and global coverage, spanning over ground, underwater and space. The sixth-generation (6G) of mobile networks are expected to burgeon in the coming decade to address these limitations. The development of 6G vision, applications, technologies and standards has already become a popular research theme in academia and the industry. In this paper, we provide a comprehensive survey of the current developments towards 6G. We highlight the societal and technological trends that initiate the drive towards 6G. Emerging applications to realize the demands raised by 6G driving trends are discussed subsequently. We also elaborate the requirements that are necessary to realize the 6G applications. Then we present the key enabling technologies in detail. We also outline current research projects and activities including standardization efforts towards the development of 6G. Finally, we summarize lessons learned from state-of-the-art research and discuss technical challenges that would shed a new light on future research directions towards 6G.

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Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems

Cited by 35 publications

References 27 publications

MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments

Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research

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