Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mukku, Lalasa; Thomas, Jyothi; Vullam, Radha; Shoba, K. Title A name given to the resource Comparative Performance Analysis of Segmentation Methods in Cervigram Images Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;5588 LNNS;pp.359-370 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-1918-4_25" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-1918-4_25</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010093881?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010093881?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mukku L., CHRIST (Deemed to be University), Bangalore, India; Thomas J., CHRIST (Deemed to be University), Bangalore, India; Vullam R., Government Maternity Hospital, Tirupati, India; Shoba K., KIDWAI Memorial Institute of Oncology, Bangalore, India Description An account of the resource One of the most common cancers of the lower female reproductive tract is cervical cancer and it is a major contributor of mortality in developing nations. Screening tests include image analysis of pap smear and colposcope pictures. In image analysis, machine learning techniques can be employed to analyze and interpret images of the cervix through segmentation and extraction of characteristics for the classification of cervix images. K-means algorithm and Gaussian mixture model are popular segmentation algorithms used in cervix region-of-interest extraction. In the context of deep network learning, segmentation means the use of deep convolution networks to accurately identify different objects or regions in an image. R-CNN and Deeplab architectures are among the most frequently employed models in deep learning for automated cervix image processing. In this paper, we have systematically reviewed machine and deep learning models popularly employed in cervical cancer identification through colposcope images. Four carefully chosen models were deployed, and their performance was comparatively analyzed. This research can be a foundation for scientists looking to develop new models for the classification and segmentation of cervical cancer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Cervigrams; Deeplab V3; GMM; K-means; Segmentation; Specular reflection Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981961917-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online