Abstract
In recent years, with the change of lifestyle in Europe and America, the incidence of breast cancer in Chinese women is increasing. In order to find the model of breast cancer image screening and diagnosis with higher accuracy and better classification performance, this paper mainly constructs the breast cancer CT image detection model and the breast cancer screening model based on the convolution and deconvolution neural network (CDNN) through the convolution neural network (CNN). In this paper, the fuzzy C-means clustering algorithm (FCM) is used to improve and optimize the image of breast cancer, and the experimental results are analyzed. The optimized kernel fuzzy C-means clustering algorithm was tested on a common dataset to segment the region of interest more accurately. Our experiments show that the new deep learning model of this paper improves the automatic classification performance of breast cancer. In this paper, the research results of deep learning are applied to the medical field, and a new method based on CNN model for breast cancer screening and diagnosis is proposed, which provides a new idea for improving the artificial intelligence assisted medical diagnosis method.
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Wang, Y., Yang, F., Zhang, J. et al. Application of artificial intelligence based on deep learning in breast cancer screening and imaging diagnosis. Neural Comput & Applic 33, 9637–9647 (2021). https://doi.org/10.1007/s00521-021-05728-x
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DOI: https://doi.org/10.1007/s00521-021-05728-x