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The Pap test is a widely used screening technique to detect abnormal cells that can become cancerous. In this research, we proposed a method for automatic segmentation and classification of cervical cancer cell images. The method uses an improved U-Net architecture to segment the image and identify the region of interest (ROI). Following segmentation, we classify the cervical cell type using ResNet50V2 and an ensemble of different pretrained models to enhance performance. We developed several pipelines for cervical cancer detection, including a normal method, with and without RES_DCGAN, before classification and segmentation tasks. The proposed method was evaluated using the Pomeranian, Herlev, and SIPaKMeD datasets. The experimental results showed that whole-cell segmentation achieved 99.53%, 88.95%, and 98.3% accuracy when RES_DCGAN was added before the segmentation. The framework achieved 96% and 95% accuracy for multi-class classification on the Pomeranian and SIPaKMeD datasets, respectively. Additionally, the Herlev dataset scored an accuracy of 91%, while SIPaKMeD achieved 99% for the binary classification of cervical cell types using the ensemble method. In conclusion, the deep learning-based segmentation and classification method demonstrated promising results for cervical cancer detection and can help pathologists diagnose the disease.<\/jats:p>","DOI":"10.1038\/s41598-026-35299-7","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T11:44:04Z","timestamp":1768304644000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Deep learning-based automatic segmentation and classification for cervical cancer detection using an improved U-Net and ensemble methods"],"prefix":"10.1038","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4790-7449","authenticated-orcid":false,"given":"Betelhem Zewdu","family":"Wubineh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2239-1076","authenticated-orcid":false,"given":"Andrzej","family":"Rusiecki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6508-0468","authenticated-orcid":false,"given":"Krzysztof","family":"Halawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"issue":"12","key":"35299_CR1","doi-asserted-by":"publisher","first-page":"779","DOI":"10.14569\/IJACSA.2023.0141280","volume":"14","author":"TL Shiny","year":"2023","unstructured":"Shiny, T. L., Parasuraman, K. A graph-cut guided ROI segmentation algorithm with lightweight deep learning framework for cervical cancer classification. Int. J. Adv. Comput. Sci. Appl. 14 (12), 779\u2013792. https:\/\/doi.org\/10.14569\/IJACSA.2023.0141280 (2023).","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"issue":"2","key":"35299_CR2","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.cmpb.2013.12.012","volume":"113","author":"T Chankong","year":"2014","unstructured":"Chankong, T., Theera-Umpon, N. & Auephanwiriyakul, S. Automatic cervical cell segmentation and classification in pap smears. Comput. Methods Programs Biomed. 113 (2), 539\u2013556. https:\/\/doi.org\/10.1016\/j.cmpb.2013.12.012 (2014).","journal-title":"Comput. Methods Programs Biomed."},{"key":"35299_CR3","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.bspc.2018.09.008","volume":"48","author":"P Wang","year":"2019","unstructured":"Wang, P. et al. Automatic cell nuclei segmentation and classification of cervical pap smear images. Biomed. Signal. Process. Control. 48, 93\u2013103. https:\/\/doi.org\/10.1016\/j.bspc.2018.09.008 (2019).","journal-title":"Biomed. Signal. Process. Control"},{"issue":"1","key":"35299_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-21692-5","volume":"12","author":"J Kim","year":"2022","unstructured":"Kim, J., Park, C. M., Kim, S. Y. & Cho, A. Convolutional neural network-based classification of cervical intraepithelial neoplasias using colposcopic image segmentation for acetowhite epithelium. Sci. Rep. 12 (1), 1\u201311. https:\/\/doi.org\/10.1038\/s41598-022-21692-5 (2022).","journal-title":"Sci. Rep."},{"issue":"9","key":"35299_CR5","doi-asserted-by":"publisher","first-page":"899","DOI":"10.14569\/IJACSA.2022.01309104","volume":"13","author":"KP Battula","year":"2022","unstructured":"Battula, K. P. & Chandana, B. S. Deep learning based cervical cancer classification and segmentation from pap smears images using an EfficientNet. Int. J. Adv. Comput. Sci. Appl. 13 (9), 899\u2013908. https:\/\/doi.org\/10.14569\/IJACSA.2022.01309104 (2022).","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"35299_CR6","doi-asserted-by":"publisher","unstructured":"Talpur, D. B. DeepCervixNet: an advanced deep learning approach for cervical cancer classification in Pap smear images, no. Cc, pp. 136\u2013148, (2024). https:\/\/doi.org\/10.21015\/vtcs.v12i1.1812","DOI":"10.21015\/vtcs.v12i1.1812"},{"key":"35299_CR7","doi-asserted-by":"publisher","unstructured":"Wubineh, B. Z., Rusiecki, A. & Halawa, K. Segmentation and classification techniques for pap smear images in detecting cervical cancer: a systematic review. 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The dataset contains retrospective and irreversibly anonymized cytology images. According to an official statement issued by the Bioethics Committee of Wroclaw Medical University to the dataset provider, studies using these anonymized retrospective data do not require ethics approval, as they do not constitute a medical experiment. The SIPaKMeD and Herlev datasets are publicly available and were collected in accordance with relevant ethical standards. The authors conducted no new experiments involving human participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical statement"}}],"article-number":"5184"}}