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- W4380854310 abstract "In this paper, we analyze the effects in medical images by applying various data augmentation techniques to deep convolutional neural network learning for classifying focal liver lesions in abdominal CT images. We apply affine transformation-based, StyleGAN, Mixup, and Augmix-based data augmentation techniques to the VGG16 convolutional neural network, respectively, to learn to classify local liver lesions into cysts, hemangiomas, and metastases. For the experiments, we validate and analyze the effect of the data augmentation through both a quantitative assessment by comparing accuracy, sensitivity, and specificity for classification results of models trained by each data augmentation technique and a qualitative assessment by observing augmented image examples and the tSNE feature distributions." @default.
- W4380854310 created "2023-06-16" @default.
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- W4380854310 date "2023-06-01" @default.
- W4380854310 modified "2023-09-27" @default.
- W4380854310 title "Effect of Data Augmentation Techniques for Classification of Focal Liver Lesions Based on Deep Convolutional Neural Networks in Abdominal CT Images" @default.
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- W4380854310 doi "https://doi.org/10.15701/kcgs.2023.29.2.1" @default.
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