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,...

Full description

Saved in:

Bibliographic Details
Published in:	Scientific reports Vol. 14; no. 1; pp. 31764 - 15
Main Authors:	Yang, Hui, Aydi, Walid, Innab, Nisreen, Ghoneim, Mohamed E., Ferrara, Massimiliano
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 30.12.2024 Nature Publishing Group Nature Portfolio
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!