Classification of Cervical Precursor Lesions via Local Histogram and Cell Morphometric Features

Nurullah Calik*, Abdulkadir Albayrak, Asl Akhan, Ilknur Turkmen, Abdulkerim Capar, Behcet Ugur Toreyin, Gokhan Bilgin, Bahar Muezzinoglu, Lutfiye Durak-Ata

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Cervical squamous intra-epithelial lesions (SIL) are precursor cancer lesions and their diagnosis is important because patients have a chance to be cured before cancer develops. In the diagnosis of the disease, pathologists decide by considering the cell distribution from the basal to the upper membrane. The idea, inspired by the pathologists' point of view, is based on the fact that cell amounts differ in the basal, central, and upper regions of tissue according to the level of Cervical Intraepithelial Neoplasia (CIN). Therefore, histogram information can be used for tissue classification so that the model can be explainable. In this study, two different classification schemes are proposed to show that the local histogram is a useful feature for the classification of cervical tissues. The first classifier is Kullback Leibler divergence-based, and the second one is the classification of the histogram by combining the embedding feature vector from morphometric features. These algorithms have been tested on a public dataset.The method we propose in the study achieved an accuracy performance of 78.69% in a data set where morphology-based methods were 69.07% and Convolutional Neural Network (CNN) patch-based algorithms were 75.77%. The proposed statistical features are robust for tackling real-life problems as they operate independently of the lesions manifold.

Original languageEnglish
Pages (from-to)1747-1757
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Volume27
Issue number4
DOIs
Publication statusPublished - 1 Apr 2023

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • cell morphometric features
  • Cervical lesions
  • cervix
  • hemotoxylen and eosin
  • Kullback-Leibler divergence
  • local histogram features

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