Classification of Cervical Cell Images into Healthy or Cancer Using Convolution Neural Network and Linear Discriminant Analysis

Mohammad Sholik*, Chastine Fatichah, Bilqis Amaliah

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Cancer of the cervix is the disease that accounts for the majority of deaths in women. This disease accounts for nearly 12% of all cancers and has a high risk of death for women worldwide. If precancerous lesions are found early, the disease can be cured. Pap smear screening is known for its reliability and effectiveness in detecting cervical cell abnormalities early, but there is a risk of errors in manual image analysis. Using deep learning approaches in the domains of medicine and healthcare can be used for decision support systems to remove bias from observations. This paper presents a framework that utilizes deep learning and techniques to reduce the dimensions of features. The suggested framework captures deep features from a convolutional neural network (CNN) model and employs a feature reduction approach using linear discriminant analysis (LDA) to ensure computational cost reduction. The feature dimension derived from the CNN model produces a huge feature space that requires a feature reduction to eliminate redundant features. The features that have been reduced by linear discriminant analysis are used for the training of three classifiers, namely SVM, MLP, and K-NN, to generate final predictions. The evaluation of the proposed framework involved the utilization of three datasets that are openly accessible: the Herlev dataset, the Mendeley dataset, and the SIPaKMeD dataset, which achieved classification accuracies of 95.65% (SVM and MLP), 100% (MLP), and 97.54 (K-NN), respectively.

Original languageEnglish
Title of host publicationProceedings of the 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages383-389
Number of pages7
ISBN (Electronic)9798350313635
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023 - Hybrid, Bali, Indonesia
Duration: 13 Jul 202315 Jul 2023

Publication series

NameProceedings of the 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023

Conference

Conference2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023
Country/TerritoryIndonesia
CityHybrid, Bali
Period13/07/2315/07/23

Keywords

  • Cervical Cancer
  • Classification
  • Convolutional Neural Network
  • Linear Discriminant Analysis

Fingerprint

Dive into the research topics of 'Classification of Cervical Cell Images into Healthy or Cancer Using Convolution Neural Network and Linear Discriminant Analysis'. Together they form a unique fingerprint.

Cite this