Shujing Sun^1,3, Jiale Wu², Jian Yao¹, Yang Cheng⁴, Xin Zhang¹, Zhihua Lu³, Pengjiang Qian^1,*

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 >

Jian Liu¹, Wantao Wang¹, Jie Chen^{2, *}, Guozhong Sun³, Alan Yang⁴

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1187-1200, 2020, DOI:10.32604/cmc.2020.05883

Abstract Classification of skin lesions is a complex identification challenge. Due to the wide variety of skin lesions, doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy. The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention. With the development of deep learning, the field of image recognition has made longterm progress. The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology. In this work, we try to classify seven kinds of lesion images by various models… More >

Ming He^1,2, Hongbin Wang^1,*, Lianke Zhou¹, Pengming Wang³, Andrew Ju⁴

CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 521-532, 2018, DOI:10.32604/cmc.2018.03710

Abstract An important issue for deep learning models is the acquisition of training of data. Without abundant data from a real production environment for training, deep learning models would not be as widely used as they are today. However, the cost of obtaining abundant real-world environment is high, especially for underwater environments. It is more straightforward to simulate data that is closed to that from real environment. In this paper, a simple and easy symmetric learning data augmentation model (SLDAM) is proposed for underwater target radiate-noise data expansion and generation. The SLDAM, taking the optimal classifier of an initial dataset as… More >