Karafan Journal

Karafan Journal

An Intelligent Ensemble Model of Uncertainty Management in Belief Network for the Classification of Microscopic Images to Detect Cervical Cancer

Document Type : Original Article

Authors
1 PhD Candidate, Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran.
2 Associate Professor Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran.
Abstract
The analysis of medical images in recent years in order to diagnose types of cancer has attracted the attention of researchers. The present research presents a comprehensive model, using the ability of belief theory and deep learning to process pap smear images, for the diagnosis of cervical cancer. In the aggregate classification model, the combination of classification models and belief network is used. In this method, it uses the belief network and Dempster's combination law to combine observations to manage the uncertainty of cell types classification. This model uses the proposed cumulative classification model in order to manage uncertainty in similar unclassified samples. This proposed method uses five classifiers including support vector machine, K-nearest neighbor, decision tree, Naive Bayesian and fuzzy classifier to classify samples and improve model performance. The results of the implementation of the proposed model were analyzed on two standard data sets. In the Sipakmed data set, this model was superior to the existing methods in terms of accuracy with 98.93%, and in the Herlev data set, it was superior to the existing methods with a sensitivity of 98.83%, and the level under the ROC chart was 99.94%. which is promising for early detection of this type of cancer. 
Keywords
Subjects

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Volume 21, Issue 1 - Serial Number 66
Engineering & Technical
Spring 2024
Pages 89-69

  • Receive Date 11 July 2023
  • Revise Date 27 February 2024
  • Accept Date 05 April 2024