Cervical cancer classification using sparse stacked autoencoder and fuzzy ARTMAP

Cervical cancer (CC) is affecting women predominantly, and early diagnosis could cure this cancer. This study aims to design and develop an effective deep learning-based classification model to detect early CC stages using clinical data. The proposed method is a combination of an unsupervised deep l...

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Veröffentlicht in:	Neural computing & applications Jg. 36; H. 22; S. 13895 - 13913
Hauptverfasser:	Liaw, Lawrence Chuin Ming, Tan, Shing Chiang, Goh, Pey Yun, Lim, Chee Peng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.08.2024 Springer Nature B.V
Schlagworte:	Artificial Intelligence Cancer Cervical cancer Classification Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Cytology Data Mining and Knowledge Discovery Datasets Deep learning Diagnosis Image Processing and Computer Vision Machine learning Missing data Neural networks Original Article Performance evaluation Probability and Statistics in Computer Science Sparsity State-of-the-art reviews Sparse stacked autoencoder Feature transformation Data sparsity Cervical cancer Fuzzy ARTMAP Classification
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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