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 Article Faculty Publications -Articles 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 Title A name given to the resource AMAA-GMM: adaptive Mexican axolotl algorithm based enhanced Gaussian mixture model to segment the cervigram images Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of Reasoning-based Intelligent Systems;Volume;18;Issue;1;pp.33-40 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJRIS.2026.150612" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJRIS.2026.150612</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105025190162?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105025190162?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), Kanmanike, Kumbalgodu, Mysore Road, Karnataka, Bangalore, 560074, India; Thomas J., CHRIST (Deemed to be University), Kanmanike, Kumbalgodu, Mysore Road, Karnataka, Bangalore, 560074, India Description An account of the resource Colposcopy is a crucial imaging technique for finding cervical abnormalities. Colposcopic image evaluation, particularly the accurate delineation of the cervix region, has considerable medical significance. Before segmenting the cervical region, specular reflection removal is an efficient approach. Cervical cancer can be found using a visual check with acetic acid that turns precancerous and cancerous areas white and these could be viewed as signs of abnormalities. Similarly, bright white regions known as specular reflections obstruct the identification of aceto-white areas and should therefore be removed. So, in this paper, specular reflection removal with segmenting the cervix region in a colposcopy image is proposed. The proposed approach consists of two main stages, namely, pre-processing and segmentation. In the pre-processing stage, specular reflections are detected and removed using a swin transformer. After that, cervical regions are segmented using an enhanced Gaussian mixture model (EGMM). For better segmentation accuracy, the best parameters of GMM are chosen via the adaptive Mexican axolotl optimisation (AMAO) algorithm. The performance of the proposed approach is analysed based on accuracy, sensitivity, specificity, Jaccard index, and dice coefficient, and the efficiency of the suggested strategy is compared with various methods. Copyright 2026 Inderscience Enterprises Ltd. Subject The topic of the resource adaptive Mexican axolotl optimisation; AMAO; deep learning; EGMM; enhanced Gaussian mixture model; Gaussian mixture models; machine learning; metaheuristics; segmentation Publisher An entity responsible for making the resource available Inderscience Publishers Relation A related resource ISSN: 17550556; Language A language of the resource English Type The nature or genre of the resource Article 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