Abstract
Cervical cancer is a very common cancer among women. Cytopathologists use a microscope to examine cell slides collected from the patient’s cervix to determine if it is cancerous. However, manually checking the slides to diagnose cancer is a very difficult task, not only time-consuming but also error-prone. A computer-aided diagnosis system can automatically and accurately screen cervical cell images. In this study, we propose a framework called DVT to perform classification tasks. We use SIPaKMeD and CRIC datasets together. On 11-class classification tasks, DVT achieves an accuracy of 87.35%. In the comparative experiment, DVT performs better than the CNN and VT models alone.
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Liu, W., Li, C., Sun, H., Hu, W., Chen, H., Grzegorzek, M. (2022). DVT: Application of Deep Visual Transformer in Cervical Cell Image Classification. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2022. Advances in Intelligent Systems and Computing, vol 1429. Springer, Cham. https://doi.org/10.1007/978-3-031-09135-3_24
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