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


Calik N., Albayrak A., Akhan A., Turkmen I., Çapar A., Töreyin B. U., ...More

IEEE Journal of Biomedical and Health Informatics, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Publication Date: 2022
  • Doi Number: 10.1109/jbhi.2022.3218293
  • Journal Name: IEEE Journal of Biomedical and Health Informatics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, Metadex, Civil Engineering Abstracts
  • Keywords: cell morphometric features, Cervical lesions, cervix, Classification algorithms, Convolutional neural networks, Feature extraction, hemotoxylen and eosin, Histograms, Image segmentation, Kullback-Leibler divergence, Lesions, local histogram features, Pathology
  • Istanbul Technical University Affiliated: Yes

Abstract

IEEECervical 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 Intraep- ithelial 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 morpho- metric features. These algorithms have been tested on a public dataset.1 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 Convo- lutional 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.