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- W4382877168 abstract "Gastric cancer is a kind of tumor with high morbidity and mortality, which seriously threatens people's health and life. It is of great significance to study the early diagnosis and screening of cancer for improving the cure rate of cancer, prolonging the survival time of patients, and reducing the economic and mental burden of patients. Because deep convolutional neural networks can effectively extract deep features of images, and gooenet and AlexNet models can perform wonderful image classification, they are selected for the diagnosis of pathological images of gastric cancer. Moreover, the GooleNet model is optimized to make it more targeted at medical pathological images, which not only ensures the diagnostic accuracy, but also significantly reduces the computational burden. The improved model has the characteristics of two kinds of network structure at the same time, and is more targeted at gastric cancer pathological sections, improving the sensitivity of gastric cancer pathological section recognition. The results show that the structure has splendid diagnostic accuracy and sensitivity up to 97. 61%, with a specificity of 99. 47 percent. The optimized model can diagnose gastric cancer more accurately, reduce the possibility of misdiagnosis and missed diagnosis due to doctors' personal reasons, and also help nurses to care and monitor patients, making the whole diagnosis and treatment process more intelligent and safe." @default.
- W4382877168 created "2023-07-02" @default.
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- W4382877168 date "2023-09-01" @default.
- W4382877168 modified "2023-10-18" @default.
- W4382877168 title "Deep learning-based gastric cancer diagnosis and clinical management" @default.
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- W4382877168 doi "https://doi.org/10.1016/j.jrras.2023.100602" @default.
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