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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Bibliographic Details
Published in:Neural computing & applications Vol. 36; no. 22; pp. 13895 - 13913
Main Authors: Liaw, Lawrence Chuin Ming, Tan, Shing Chiang, Goh, Pey Yun, Lim, Chee Peng
Format: Journal Article
Language:English
Published: London Springer London 01.08.2024
Springer Nature B.V
Subjects:
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 Access:Get full text
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Summary: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 learning and a supervised neural network, i.e. sparse stacked autoencoder (SSAE) and fuzzy adaptive resonance theory MAP (FAM), respectively, and is denoted as SSAE-FAM. Specifically, SSAE is applied to tackle the data sparsity problem. It extracts the representative features from a data set through feature transformation. The transformed features are then classified by FAM. In this study, a CC data set obtained from the University of California Irvine (UCI) machine learning repository is utilised for evaluation. Owing to missing data in the original CC data set, two data sets are generated from the original CC data samples using two data preprocessing techniques. Both generated CC data sets with four target classes (i.e. Schiller, Cytology, Biopsy, and Hinselmann) are evaluated as four independent binary-class problems. We improve the classification performance of FAM by mitigating the data sparsity problem. Based on a series of experimental studies, SSAE-FAM outperforms other state-of-art methods by achieving 99.47%, 99.34%, 99.48%, and 99.81% mean accuracy rates, respectively, with the first CC data set, and 99.74%, 99.86%, 99.77%, and 99.80% mean accuracy rates, respectively, with the second CC data set. The results positively indicate the usefulness of SSAE-FAM for early CC diagnosis.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-024-09706-x