Abstract
How to correctly and efficiently detect and identify tumors has always been a core issue in the medical field. In order to improve the accuracy and efficiency of tumor detection, we proposed a DenseNet-based tumor recognition and classification model Cyclic DenseNet. First, Cyclic DenseNet inherits the fully-connected architecture of DenseNet and fully exploits the image features. Secondly, Cyclic DenseNet introduces the group concept and uses Group Normalization instead of Batch Normalization, in order to improve the stability of the parameters. At the same time, we extract the features deep after each Dense Block, which reinforces the features reuse and strengthens the advantages of DenseNet. Finally, in order to reduce the high time overhead caused by the reuse of multiple features, we adopt the optimal block extraction method, which greatly improves the training efficiency. The experimental results show that this method can effectively improve the accuracy and efficiency of tumor detection and recognition. Compared with the existing algorithms, it has achieved better results in the area under the receiver operating curve (ROC) and other criteria.
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Jiang, D., Liu, H., Guo, Q., Zhang, C. (2019). Cyclic DenseNet for Tumor Detection and Identification. In: Vaidya, J., Zhang, X., Li, J. (eds) Cyberspace Safety and Security. CSS 2019. Lecture Notes in Computer Science(), vol 11983. Springer, Cham. https://doi.org/10.1007/978-3-030-37352-8_42
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DOI: https://doi.org/10.1007/978-3-030-37352-8_42
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