Shujing Sun1,3, Jiale Wu2, Jian Yao1, Yang Cheng4, Xin Zhang1, Zhihua Lu3, Pengjiang Qian1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 923-938, 2023, DOI:10.32604/cmes.2023.027356
Abstract Many existing intelligent recognition technologies require huge datasets for model learning. However, it is not easy to collect rectal cancer images, so the performance is usually low with limited training samples. In addition, traditional rectal cancer staging is time-consuming, error-prone, and susceptible to physicians’ subjective awareness as well as professional expertise. To settle these deficiencies, we propose a novel deep-learning model to classify the rectal cancer stages of T2 and T3. First, a novel deep learning model (RectalNet) is constructed based on residual learning, which combines the squeeze-excitation with the asymptotic output layer and new cross-convolution layer links in the… More >
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