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http://dx.doi.org/10.9718/JBER.2018.39.6.229

Malignant and Benign Classification of Liver Tumor in CT according to Data pre-processing and Deep running model  

Choi, Bo Hye (Bio-Medical Engineering, College of Health Science, Gachon University)
Kim, Young Jae (Department of Biomedical Engineering, College of Medicine, Gachon University)
Choi, Seung Jun (Department of Radiology, Gachon University Gil Hospital)
Kim, Kwang Gi (Department of Biomedical Engineering, College of Medicine, Gachon University)
Publication Information
Journal of Biomedical Engineering Research / v.39, no.6, 2018 , pp. 229-236 More about this Journal
Abstract
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 $200{\times}200$ image size is 69.58% (AlexNet), 69.4% (VggNet), $150{\times}150$ image size is 71.54%, 67%, $100{\times}100$ 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.
Keywords
Liver cancer; Deep learning; AlexNet; VggNet; Classification;
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