Classification of cervical cancer using Dense CapsNet with Seg-UNet and denoising autoencoders

Classification of cervical cancer using Dense CapsNet with Seg-UNet and denoising autoencoders

Abstract Cervical cancer is one of the deadly diseases that affects women, which requires periodic examinations to identify and treat any cancerous tumors at a preliminary stage. The most prevalent examination tool for cervical cancer prompt identification is the cervical smear (Pap smear) testing;...

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Bibliographic Details
Main Authors: Hui Yang, Walid Aydi, Nisreen Innab, Mohamed E. Ghoneim, Massimiliano Ferrara
Format: Article
Language:English
Published: Nature Portfolio 2024-12-01
Series:Scientific Reports
Subjects:
Cervical cancer
Deep learning
Segmentation
Autoencoders
Dense CapsNet
Online Access:https://doi.org/10.1038/s41598-024-82489-2
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