Citation: Kalbhor, M.; Shinde, S. ColpoClassifier: A Hybrid Framework for Classification of the Cervigrams. Diagnostics 2023, 13, 1103. https://doi.org/10.3390/ diagnostics13061103 Academic Editor: Zoard T Krasznai Received: 6 February 2023 Revised: 3 March 2023 Accepted: 6 March 2023 Published: 14 March 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). diagnostics Article ColpoClassifier: A Hybrid Framework for Classification of the Cervigrams Madhura Kalbhor and Swati Shinde * Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune 411044, India; madhura.kalbhor@pccoepune.org * Correspondence: swati.shinde@pccoepune.org 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. Keywords: colposcopy; feature extraction; machine learning; feature fusion; GLCM; GLRLM; HOG 1. Introduction Cervical cancer affects the cervix of the vagina. Human Papillomavirus (HPV) is a major cause of cervical cancer. Other causes include smoking, sexually transmitted infections, and immune system dysfunction. Early detection of cervical cancer plays an important role in its prevention and treatment. Cervical cancer screening is necessary for early detection; however, less developed countries lack effective screening programs [1]. A Pap smear is the most common screening procedure that distinguishes abnormal cells and predicts the risk of cervical cancer. However, the Pap smear has limitations as incidences of both false negatives and false positives are high. If a Pap smear can predict the possibility of cancer with its limitations, then we suggest a colposcopy for a more accurate diagnosis. Colposcopy is a well-suited procedure for precancerous examination of the cervix that can also be used in a low-cost setting. The availability of professionals and technology plays an important role in the diagnosis and treatment of cervical cancer [1,2]. Colposcopy is a very subjective process as it depends on the knowledge and experience of the doctors. The primary aim of this test is to identify premalignant or malignant lesions as well as genital warts, polyps, and infections [3]. Visual inspection involves the application of acetic acid to the visible part of the cervix. The classification of a cervigram involves analyzing complicated patterns. Image analysis and machine learning methods are used widely in the medical field. These methods assist doctors by providing them with reasonable diagnoses. Primary features to detect abnormal cervix are aceto-whitening of the cervix, punctuations and mosaic patterns, erosion, and a rough surface [4]. Diagnostics 2023, 13, 1103. https://doi.org/10.3390/diagnostics13061103 https://www.mdpi.com/journal/diagnostics