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- W3157688006 abstract "The world is changing, and we are surrounded by intelligent machines helping us navigate our day to day activities; whether its predictive text while writing email, or a GPS guiding us through the fastest route, there is a man-made intelligent machine. Broadly all of this comes under artificial intelligence framework. Deep learning is a specialized branch of artificial intelligence built on how humans learn, giving it a mammoth learning and generalization capacity. Deep learning outperforms traditional learning algorithms by its ability to add/remove neurons to maximize the outcomes. With the technological advancement such as advanced imaging techniques and sequencing, the importance of such intelligent machines is on the rise. For complex diseases such as cancer, cardiovascular disease, and neurological conditions the diagnosis and treatment are gearing towards incorporating machine learning models for better diagnosis, prognosis, and treatment regime. In this chapter, we will explore deep learning models, and how it is being used for health care research and innovation. Finally, we will demonstrate building up a deep learning model for clinical application." @default.
- W3157688006 created "2021-05-10" @default.
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- W3157688006 date "2021-05-19" @default.
- W3157688006 modified "2023-10-18" @default.
- W3157688006 title "Artificial Intelligence and Deep Learning for Medical Diagnosis and Treatment" @default.
- W3157688006 cites W2948732750 @default.
- W3157688006 doi "https://doi.org/10.1201/9781003093770-11" @default.
- W3157688006 hasPublicationYear "2021" @default.
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