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

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 memb...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2023-04, Vol.27 (4), p.1747-1757
Main Authors: Calik, Nurullah, Albayrak, Abdulkadir, Akhan, Asl, Turkmen, Ilknur, Capar, Abdulkerim, Toreyin, Behcet Ugur, Bilgin, Gokhan, Muezzinoglu, Bahar, Durak-Ata, Lutfiye
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Cancer
cell morphometric features
Cervical lesions
cervix
Classification
Classification algorithms
Convolutional neural networks
Diagnosis
Embedding
Feature extraction
hemotoxylen and eosin
Histograms
Humans
Image segmentation
Kullback-Leibler divergence
Lesions
local histogram features
Neck
Neural networks
Neural Networks, Computer
Pathology
Precursors
Research Design
Tissues
Citations: Items that this one cites
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Summary: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.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2022.3218293