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

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; however,...

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Veröffentlicht in:	Scientific reports Jg. 14; H. 1; S. 31764 - 15
Hauptverfasser:	Yang, Hui, Aydi, Walid, Innab, Nisreen, Ghoneim, Mohamed E., Ferrara, Massimiliano
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 30.12.2024 Nature Publishing Group Nature Portfolio
Schlagworte:	692/699/75 692/700/228 Algorithms Autoencoders Cervical cancer Classification Deep Learning Dense CapsNet Female Humanities and Social Sciences Humans Image processing Image Processing, Computer-Assisted - methods Machine Learning Medical research multidisciplinary Negligence Neural Networks, Computer Probability learning Science Science (multidisciplinary) Segmentation Uterine Cervical Neoplasms - classification Uterine Cervical Neoplasms - diagnosis Uterine Cervical Neoplasms - pathology Deep learning Dense CapsNet Segmentation Autoencoders Cervical cancer
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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