Abstract
Gallbladder cancer is a relatively rare but highly malignant tumor. This study mainly explores the CT findings of gallbladder cancer based on neural networks. This study designed a gallbladder cancer LDCT image denoising network. Ability to process different doses of gallbladder cancer LDCT images with significant differences in noise and artifact distribution, this study designed the noise level estimation sub-network as a codec structure; the decoding part is used to generate the noise level of the gallbladder cancer LDCT image Artifact image. Artificial neural network is a kind of artificial neural network that simulates the behavior characteristics of animal neural network and achieves the purpose of processing information by adjusting the interconnection between a large number of internal nodes. In order to meet the requirements of medical diagnosis for gallbladder cancer LDCT image quality, this study designed the backbone noise reduction network as a GAN framework that can be internally optimized. The discriminator network structure of this study is a multi-scale inception structure. As a sub-network of GAN, the discriminator network is used to distinguish true and false images and constrain the generator to make the generated images close to real images. In addition, it can be used as a noise evaluation sub-network to evaluate the noise gallbladder cancer LDCT. The treatment methods of gallbladder cancer include surgery, chemotherapy, radiation therapy, arterial interventional perfusion therapy, targeted therapy, etc. Surgery is currently the first choice for the treatment of gallbladder cancer, and the choice of surgery depends on the stage and growth site of gallbladder cancer. The image denoising network was used to evaluate the quality of the noise-reduced image. The average precision of GAN network for gallbladder cancer area is 91.0%, and the highest value is 95.2%. This study will provide a reliable reference value for the auxiliary diagnosis of gallbladder cancer.
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Chang, Y., Wu, Q., Chi, L. et al. CT manifestations of gallbladder carcinoma based on neural network. Neural Comput & Applic 35, 2039–2044 (2023). https://doi.org/10.1007/s00521-022-06973-4
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DOI: https://doi.org/10.1007/s00521-022-06973-4