발간년도 : [2017]
| 논문정보 |
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| 논문명(한글) |
[Vol.12, No.5] Recent Trend of Biomedical Image Classification Methods |
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| 논문투고자 |
Seungyeon Shin, Hyunjin Kim, Sanghyun Park |
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| 논문내용 |
As biomedical imaging equipment and machine learning algorithms are improved, biomedical image analysis became a popular topic for both biologists and machine learning researchers. Biomedical image analysis includes various topics such as classification, segmentation, and registration. All of them are being actively studied, and there are a lot of remarkable papers on these topics. In this paper, we focus on recent trend of biomedical image classification. Because researchers use microscopy images for biological image analysis and use radiological data such as CT, MRI for medical image analysis, we explain classification methods used in several researches for biological image and medical image separately, depending on the type of images to be analyzed. In addition to traditional methods based on feature descriptor, we also introduce methods that apply deep learning in biomedical image classification, since deep learning is recently used in many researches for image processing. We found that deep learning based models show great performance in biomedical domain, and state-of-the-art idea of image processing and computer vision has potential to be applied to biomedical problems. Finally we suggest future works for better biomedical image classifier, based on the idea that are recently studied in computer vision, but with few papers in biomedical domain. |
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