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

Nurullah Calik, Abdulkadir Albayrak, Asl Akhan

    IEEE Journal of Biomedical and Health Informatics
    |November 1, 2022
    PubMed
    Summary

    This study introduces a novel method for classifying cervical tissues using local histogram features, achieving 78.69% accuracy. This approach aids in diagnosing cervical squamous intra-epithelial lesions (SIL) precursor cancer lesions more effectively.

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    Area of Science:

    • Pathology
    • Computer-Aided Diagnosis
    • Biomedical Image Analysis

    Background:

    • Cervical squamous intra-epithelial lesions (SIL) are precancerous conditions requiring accurate diagnosis for effective treatment.
    • Current diagnostic methods rely on pathologists' assessment of cell distribution within cervical tissues.
    • Understanding cell distribution patterns across tissue layers is crucial for identifying Cervical Intraepithelial Neoplasia (CIN) grades.

    Purpose of the Study:

    • To propose and evaluate novel classification schemes for cervical tissues utilizing local histogram features.
    • To demonstrate the utility of histogram information for explainable tissue classification in cervical cancer screening.
    • To compare the performance of the proposed methods against existing morphology-based and Convolutional Neural Network (CNN) approaches.

    Main Methods:

    • Development of two distinct classification schemes based on local histogram analysis.
    • Implementation of a Kullback Leibler divergence-based classifier.
    • Integration of morphometric features with histogram data for a combined classification approach.

    Main Results:

    • The proposed method achieved a classification accuracy of 78.69% on a public dataset.
    • This performance surpasses traditional morphology-based methods (69.07%) and CNN patch-based algorithms (75.77%).
    • The statistical features derived from local histograms proved robust and independent of lesion characteristics.

    Conclusions:

    • Local histogram analysis is a valuable and effective feature for classifying cervical tissues.
    • The proposed explainable AI approach offers improved diagnostic accuracy for cervical lesions.
    • These findings support the potential of histogram-based statistical features in real-world clinical applications for cervical cancer prevention.