Topic: Multimedia communications in eHealthIntroduction, Main Report Body and Conclusion. The Main Report Body should consist of three parts: – Application itself – Multimedia techniques and/or...

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Topic: Multimedia communications in eHealthIntroduction, Main Report Body and Conclusion. The Main Report Body should consist of three parts: – Application itself – Multimedia techniques and/or technology used in the application – Communication techniques and/or technology used in the application


Answered 1 days AfterJun 02, 2022

Answer To: Topic: Multimedia communications in eHealthIntroduction, Main Report Body and Conclusion. The Main...

Tanisha answered on Jun 03 2022
82 Votes
REPORT
Introduction
    Around two thirds of the world’s deaths are caused by some chronic diseases such as cancer, diabetes, respiratory diseases etc. Such diseases have put a burden on the functioning of traditional healthcare systems. To offer better health care facilities, many researches are done to improvise the performance of diagnostic systems and other medical applications. Healthcare applica
tions allow for overcoming the patients’ needs such as deficiency in the number of specialist doctors, helping the remote areas by providing online services, pre booking of appointments to avoid the medical queues etc. Nowadays, smart healthcare systems are used for monitoring the patients’ records. In both medical and non-medical applications, patient’s signals are measured based on the wearable devices and other technologies such as IoT, wireless communications, cloud edge computing, machine learning algorithms, 5G, AI etc. Applications that target preventive healthcare are using mobile applications that focus on detecting the patterns of the patients’ data for diabetes, cardiac diseases, obesity etc. The real human activities are recorded to ensure safety measures to avoid any medical falls and we use mobile sensors and wearable devices are used for the ADL (Activities of Daily Life). Wearable devices such as internal sensors at different body locations can help us to get the insights of the data through a wireless sensor networks. With the advance in IT technologies, we have an opportunity to analyze multimedia data from the human body. Benefits of multimedia data comprised of less pruning towards the loss and exchanging the data and maintaining it in more secured manner. In today’s era, large amount of multimedia data are used to derive the analysis of medical characteristics. With this, there is a tremendous amount of computational parallel power using graphical processing units, GPU architecture for the matrix and CPU operations. For the Covid-19 pandemic, data analysis is done through the chest tomography scans which are deeply dependent on the neural networks. We will see the use cases of deep learning in the medical imaging application.
Electronic Heatlh Care Application for Medical Imaging and Diagnostics
    New technologies with the help of multimedia data have helped in removing the traditional healthcare system. Now digital techniques are getting advance in the medical imaging and diagnostic applications. Such techniques are driven by powerful big data and patter recognition mechanism which uses machine learning and deep learning technologies. When a neural network is fed with the multimedia data that has attributes consisting of patient’s medical information, it finally delivers a solution for diagnosis and predicts correct analysis. For example: Techniques of deep learning has helped in getting the insights from the application which is sort of related to the visual feature extraction, textual analysis or identification of speeches. This requires healthcare monitoring stuff that uses the wearable electronic devices that is transferring the data to the mobile applications. Deep Learning Techniques help us to build the model which interprets the medical images well such as MRI scan, CT scan, X-ray etc. to perform any medical diagnosis. Such models give us faster diagnostic power and treat the medical diseases properly. It builds up the neural networks that are based on the structure of functioning of the brain by taking the inspiration from neuroscience. It learns from the classification of the data and analyzes the information from different sections of modalities to get some logical connections for the accurate data. This data consist of raw information coming from different sensors where a modality is build based on the hierarchical representation. These approaches helped to handle multi modality concept, extract better insights and analyze the correct data patterns for prediction. DNN identifies clinical notes which are like in the form of...
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