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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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Liver cancer is one of the highest incidents in the world, and the mortality rate is the second most common disease after lung cancer. The purpose of this study is to evaluate the diagnostic ability of deep learning in the classification of malignant and benign tumors in CT images of patients with liver tumors. We also tried to identify the best data processing methods and deep learning models for classifying malignant and benign tumors in the liver. In this study, CT data were collected from 92 patients (benign liver tumors: 44, malignant liver tumors: 48) at the Gil Medical Center. The CT data of each patient were used for cross-sectional images of 3,024 liver tumors. In AlexNet and VggNet, the average of the overall accuracy at each image size was calculated: the average of the overall accuracy of the 200 × 200 image size is 69.58% (AlexNet), 69.4% (VggNet), 150 × 150 image size is 71.54%, 67%, 100 × 100 image size is 68.79%, 66.2%. In conclusion, the overall accuracy of each does not exceed 80%, so it does not have a high level of accuracy. In addition, the average accuracy in benign was 90.3% and the accuracy in malignant was 46.2%, which is a significant difference between benign and malignant. Also, the time it takes for AlexNet to learn is about 1.6 times faster than VggNet but statistically no different (p > 0.05). Since both models are less than 90% of the overall accuracy, more research and development are needed, such as learning the liver tumor data using a new model, or the process of pre-processing the data images in other methods. In the future, it will be useful to use specialists for image reading using deep learning.
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참고문헌 (16)
참고문헌 신청
심재준 / 2017 / 국내 간암 연구 현황 / Journal of Liver Cancer 17(2):105~110
National Cancer Information Center
이명아 / 2010 / 간암의 조기진단 / 대한내과학회지 79(3):224~230
T. S.Furey / 2000 / Support vector machine classification and validation of cancer tissue samples using microarray expression data / Bioinformatics 16(10):906~914
M. Längkvist / 2016 / Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks / Remote Sensing 8(4):329
National Cancer Information Center
이명아 / 2010 / 간암의 조기진단 / 대한내과학회지 79(3):224~230
T. S.Furey / 2000 / Support vector machine classification and validation of cancer tissue samples using microarray expression data / Bioinformatics 16(10):906~914
M. Längkvist / 2016 / Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks / Remote Sensing 8(4):329
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