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Publication: IIP Proceedings Year: 2022, Month: November Page No: 255-261, ISSN/ISBN: 978-93-95632-70-6, DOI/Link: https://www.rsquarel.org/assets/docupload/rsl2023F0079F829E1446A.pdf
A SURVEY ON ARTIFICIAL INTELLIGENCE IN RADIOLOGY
Area/Stream: Artificial Intelligence, Authors: Vinayak Y G, Tushar Suhas Pati, Dr. Jyothi A P Keywords: Radiology, Artificial Intelligence (AI), Deep Learning (DL), Long Short-Term Memory (LSTM) Book Name /series: Futuristic Trends in Artificial Intelligence, Volume 2, Book 16, Chapter 27Publication: IIP Proceedings Year: 2022, Month: November Page No: 255-261, ISSN/ISBN: 978-93-95632-70-6, DOI/Link: https://www.rsquarel.org/assets/docupload/rsl2023F0079F829E1446A.pdf
Abstract:
Radiology plays a major role in initial stage by diagnosing the cause of symptoms in a patient. There are various techniques used in Radiology such as X-Rays, Computed Tomography (CT) scan, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) scan, etc. One can use Artificial Intelligence (AI) algorithms such as Deep Learning, to enhance the outcome of the scan results. Deep Learning methods such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Auto-Encoder have found application in areas where medical images are involved. As compared to traditional methods where radiologist had to manually analyze the images to assist the doctors; AI has helped in automating these tasks. In this article, we have discussed various proposed Deep Learning techniques that are available and being used by radiology department to get accurate and enhanced results. We explore the positive impact and limitations of the existing systems. Finally, we conclude with discussion on further areas of improvement.
Cite this: Vinayak Y G, Tushar Suhas Pati, Dr. Jyothi A P,"A SURVEY ON ARTIFICIAL INTELLIGENCE IN RADIOLOGY", Futuristic Trends in Artificial Intelligence, Volume 2, Book 16, Chapter 27, November, 2022, 255-261, 978-93-95632-70-6, https://www.rsquarel.org/assets/docupload/rsl2023F0079F829E1446A.pdf
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