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Medical Image Analysis Using Artificial Intelligence
Prog. Med. Phys. 2019;30(2):49-58
Published online June 30, 2019
© 2019 Korean Society of Medical Physics.

Hyun Jin Yoon1,2, Young Jin Jeong1,2, Hyun Kang2, Ji Eun Jeong1, Do-Young Kang1,2

1Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine, 2Institute of Convergence Bio-Health, Dong-A University, Busan, Korea
Correspondence to: Do-Young Kang (dykang@dau.ac.kr)
Tel: 82-51-240-5630
Fax: 82-51-242-7237
Received May 2, 2019; Revised May 21, 2019; Accepted May 21, 2019.
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. 
Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. 
Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. 
Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.
Keywords : Artificial Intelligence (AI), Medical images, Deep-learning, Machine-learning, Convolutional Neural Network (CNN), Big data


June 2019, 30 (2)