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- W2894031473 abstract "Classification of bone tumor plays an important role in treatment. As artificial diagnosis is in low efficiency, an automatic classification system can help doctors analyze medical images better. However, most existing methods cannot reach high classification accuracy on clinical images because of the high similarity between images. In this paper, we propose a super label guided convolutional neural network (SG-CNN) to classify CT images of bone tumor. Images with two hierarchical labels would be fed into the network, and learned by its two sub-networks, whose tasks are learning the whole image and focusing on lesion area to learn more details respectively. To further improve classification accuracy, we also propose a multi-channel enhancement (ME) strategy for image preprocessing. Owing to the lack of suitable public dataset, we introduce a CT image dataset of bone tumor. Experimental results on this dataset show our SG-CNN and ME strategy improve the classification accuracy obviously." @default.
- W2894031473 created "2018-10-05" @default.
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- W2894031473 date "2018-01-01" @default.
- W2894031473 modified "2023-09-25" @default.
- W2894031473 title "Classification of Bone Tumor on CT Images Using Deep Convolutional Neural Network" @default.
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- W2894031473 doi "https://doi.org/10.1007/978-3-030-01421-6_13" @default.
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