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• W4385372961 abstract "A new phase in healthcare is rapidly approaching where “deep learning” deployment in the health system is becoming more and more popular. This chapter discusses about “big data” and how it helps to manage healthcare issues and looks into the challenges and threats in using “deep learning” in healthcare practices. A better understanding, determination, and treatment of numerous diseases have been made possible by “big data” from the healthcare industry, which is utilized in “deep learning” process. “Deep learning” is also a type of machine learning; there are layers of artificial neural networks (ANNs) through which data are filtered from each successive structured layer to inform the output for the next layer. Deep models make it possible to find high-level characteristics, which enhances performance compared to typical models, increases interpretability, and offers more insight into the structure of biological data. Digital technology proved to be essential in the “COVID-19” response, globally. “Deep learning” neural networks were used to screen, track, and anticipate future events during the “COVID-19” pandemic. More recently, researchers have used multilayer perceptron algorithm (MLP) to detect, diagnose, and treat the condition and the Inception C-Net (IC-Net) has been used for the detection and differentiation of COVID-19 from other diseases. However, further studies should be conducted to reduce the risk of data breaches and increase online security for future “big data”–based healthcare management systems." @default.
• W4385372961 created "2023-07-29" @default.
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• W4385372961 date "2023-01-01" @default.
• W4385372961 modified "2023-10-16" @default.
• W4385372961 title "“Deep learning” for healthcare: Opportunities, threats, and challenges" @default.
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