Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing

Application of Artificial Intelligence in Medical Imaging

Author(s): Sampurna Panda, Rakesh Kumar Dhaka and Babita Panda *

Pp: 19-32 (14)

DOI: 10.2174/9789815079210123010005

* (Excluding Mailing and Handling)


The emergence of the Internet of Things (IoT) and Artificial Intelligence (AI) applications in many industries is due to recent developments in technology and connectivity. This paper outlines various industry initiatives in healthcare that utilize machine learning techniques. To meet this rising demand, considerable investment is required to develop new medical imaging algorithms, such as those that can be used to diagnose disease diagnostic systems errors, which can yield ambiguous medical treatments. Early disease in imaging is usually predicted by machine learning and deep learning algorithms. Imaging tools use machine learning and deep learning techniques to analyze early disease. Medical imaging is on the cutting edge of deep learning techniques, specifically the application of convolution neural networks. The supervised or unsupervised algorithms are applied to a dataset containing specific instances, and then the predictions are displayed. Machines and deep learning approaches are excellent for data classification and automated decision-making.

Keywords: Artificial Intelligence, Deep Learning, Internet of Things (IoT), Machine Learning, Neural Network.

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