Comparative Performance Analysis of Segmentation Methods in Cervigram Images
Cervigrams; Deeplab V3; GMM; K-means; Segmentation; Specular reflection
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.
Mukku, Lalasa; Thomas, Jyothi; Vullam, Radha; Shoba, K.
Lecture Notes in Networks and Systems;Volume;5588 LNNS;pp.359-370
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-96-1918-4_25 https://www.scopus.com/pages/publications/105010093881?origin=resultslist
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