한국의학물리학회지:의학물리 (Progress in Medical Physics)

  • 제30권2호
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  • Pages.49-58
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  • 2019
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  • 2508-4445(pISSN)
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  • 2508-4453(eISSN)
한국의학물리학회 (Korean Society of Medical Physics)
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Medical Image Analysis Using Artificial Intelligence

  • Yoon, Hyun Jin (Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine) ;
  • Jeong, Young Jin (Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine) ;
  • Kang, Hyun (Institute of Convergence Bio-Health, Dong-A University) ;
  • Jeong, Ji Eun (Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine) ;
  • Kang, Do-Young (Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine)
  • 투고 : 2019.05.02
  • 심사 : 2019.05.21
  • 발행 : 2019.06.30
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초록

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.

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