ColpoClassifier: A Hybrid Framework for Classification of the Cervigrams

Overview

Journal Diagnostics (Basel)

Specialty Radiology

Date 2023 Mar 29

PMID 36980411

Authors

Madhura Kalbhor

Swati Shinde

Affiliations

Soon will be listed here.

Abstract

Colposcopy plays a vital role in detecting cervical cancer. Artificial intelligence-based methods have been implemented in the literature for the classification of colposcopy images. However, there is a need for a more effective method that can accurately classify cervigrams. In this paper, ColpoClassifier, a hybrid framework for the classification of cervigrams, is proposed, which consists of feature extraction followed by classification. This paper uses a Gray-level co-occurrence matrix (GLCM), a Gray-level run length matrix (GLRLM), and a histogram of gradients (HOG) for feature extraction. These features are combined to form a feature fusion vector of the form GLCM + GLRLM + HOG. The different machine learning classifiers are used for classification by using individual feature vectors as well as feature fusion vectors. The dataset used in this paper is compiled by downloading images from the WHO website. Two variants of this dataset are created, Dataset-I contains images of the aceto-whitening effect, green filter, iodine application, and raw cervigram while Dataset-II only contains images of the aceto-whitening effect. This paper presents the classification performance on all kinds of images with the individual as well as hybrid feature fusion vector and concludes that hybrid feature fusion vectors on aceto-whitening images have given the best results.

Citing Articles

Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer.

Hamdi M, Senan E, Awaji B, Olayah F, Jadhav M, Alalayah K Diagnostics (Basel). 2023; 13(15).

PMID: 37568901 PMC: 10416962. DOI: 10.3390/diagnostics13152538.

Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min-Max Neural Network for Cervical Cancer Diagnosis.

Kalbhor M, Shinde S, Popescu D, Hemanth D Diagnostics (Basel). 2023; 13(7).

PMID: 37046581 PMC: 10093705. DOI: 10.3390/diagnostics13071363.

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