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- W4361761874 abstract "Cervical cancer is a type of malignant tumor, seriously and extensively endangering women’s life. Early diagnosis and treatment can effectively reduce its morbidity and mortality. Therefore, regular screening for cervical cancer is a necessary mean for women to maintain their health. Traditional cervical cancer detection uses a combined method of cervical smear and manual reading. However, multiple factors such as different film producing technology, disparate sampling techniques, the knowledge and attitude of a doctor, can easily affect the accuracy of the test results. In this paper, we propose several convolutional neural networks with different architectures to classify cervical cells on the SIPAKMeD dataset into five categories. The result shows that the accuracy of the VGG16 with SENET module obtains a classification accuracy of 96.57% on SIPAKMeD dataset; adopting attention modules is an efficacious way to improve the classification accuracy of a CNN." @default.
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- W4361761874 date "2023-01-01" @default.
- W4361761874 modified "2023-09-25" @default.
- W4361761874 title "Deep Convolutional Neural Network Based Cervical Cancer Exfoliated Cell Detection" @default.
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- W4361761874 doi "https://doi.org/10.1007/978-981-99-0923-0_59" @default.
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