Prog Med Phys.  2019 Jun;30(2):49-58. 10.14316/pmp.2019.30.2.49.

Medical Image Analysis Using Artificial Intelligence

Affiliations
  • 1Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine, Busan, Korea. dykang@dau.ac.kr
  • 2Institute of Convergence Bio-Health, Dong-A University, Busan, Korea.

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.

Keyword

Artificial Intelligence (AI); Medical images; Deep-learning; Machine-learning; Convolutional Neural Network (CNN); Big data

MeSH Terms

Abdomen
Artificial Intelligence*
Breast
Classification
Diagnosis
Heart
Lung
Methods
Pathology
Retina

Figure

  • Fig. 1 Medical image diagnosis method using artificial intelligence (AI).

  • Fig. 2 Flow chart of medical imaging AI diagnosis system development.

  • Fig. 3 Developed SortDB ver1.0 for DICOM, hdr/img, nii image file viewer, delineation, cropping, and file conversion function. The environment (a) and the functional flowchart (b) of SortDB ver1.0 are displayed. Dicom PET images can be converted to multi-frame nii files, jpg, gif and save, and the preprocessing process for normalization can be performed quickly and conveniently.


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