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 Conference Papers 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/. Title A name given to the resource A Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer Subject The topic of the resource Cervical cancer; Deep learning; Expectation maximization; Gaussian mixture modelling; Lesion feature extraction; Segmentation Description An account of the resource Latest innovations in technology and computer science have opened up ample scope for tremendous advances in the healthcare field. Automated diagnosis of various medical problems has benefitted from advances in machine learning and deep learning models. Cancer diagnosis, prognosis prediction and classification have been the focus of an immense amount of research and development in intelligent systems. One of the major concerns of health and the reason for mortality in women is cervical cancer. It is the fourth most common cancer in women, as well as one of the top reasons of mortality in developing countries. Cervical cancer can be treated completely if it is diagnosed in its early stages. The acetowhite lesions are the critical informative features of the cervix. The current study proposes a novel feature engineering strategy called lesion feature extraction (LFE) followed by a lesion recognition algorithm (LRA) developed using a deep learning strategy embedded with a Gaussian mixture model with expectation maximum (EM) algorithm. The model performed with an accuracy of 0.943, sensitivity of 0.921 and specificity of 0.891. The proposed method will enable early, accurate diagnosis of cervical cancer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Creator An entity primarily responsible for making the resource Mukku L.; Thomas J. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-868, pp. 63-75. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-99-9037-5_6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-9037-5_6</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189544119&amp;doi=10.1007%2F978-981-99-9037-5_6&amp;partnerID=40&amp;md5=d88ec727023159202c272d46379d2b4f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189544119&amp;doi=10.1007%2f978-981-99-9037-5_6&amp;partnerID=40&amp;md5=d88ec727023159202c272d46379d2b4f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981999036-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper 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) Kengeri, Bangalore, 560074, India; Thomas J., CHRIST (Deemed to be University) Kengeri, Bangalore, 560074, India