{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T03:05:40Z","timestamp":1792811140456,"version":"4.5.0"},"reference-count":35,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diagnostic Cytopathology"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>Cervical cancer, a prevalent and deadly disease among women, comes second only to breast cancer, with over 700 daily deaths. The Pap smear test is a widely utilized screening method for detecting cervical cancer in its early stages. However, this manual screening process is prone to a high rate of false\u2010positive outcomes because of human errors. Researchers are using machine learning and deep learning in computer\u2010aided diagnostic tools to address this issue. These tools automatically analyze and sort cervical cytology and colposcopy images, improving the precision of identifying various stages of cervical cancer.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methodology<\/jats:title><jats:p>This article uses state\u2010of\u2010the\u2010art deep learning methods, such as ResNet\u201050 for categorizing cervical cancer cells to assist medical professionals. The method includes three key steps: preprocessing, segmentation using k\u2010means clustering, and classifying cancer cells. The model is assessed based on performance metrics viz; precision, accuracy, kappa score, precision, sensitivity, and specificity. In the end, the high success rate shows that the ResNet50 model is a valuable tool for timely detection of cervical cancer.<\/jats:p><\/jats:sec><jats:sec><jats:title>Outputs<\/jats:title><jats:p>In conclusion, the infected cervical region is pinpointed using spatial K\u2010means clustering and preprocessing operations. This sequence of actions is followed by a progressive learning technique. The Progressive Learning technique then proceeded through several stages: Stage 1 with 64 \u00d7\u200964 images, Stage 2 with 224 \u00d7\u2009224 images, Stage 3 with 512 \u00d7\u2009512 images, and the final Stage 4 with 1024 \u00d7\u20091024 images. The outcomes show that the suggested model is effective for analyzing Pap smear tests, achieving 97.4% accuracy and approx. 98% kappa score.<\/jats:p><\/jats:sec>","DOI":"10.1002\/dc.25295","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T09:24:52Z","timestamp":1711099492000},"page":"313-324","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Improving cervical cancer classification in <scp>PAP<\/scp> smear images with enhanced segmentation and deep progressive learning\u2010based techniques"],"prefix":"10.1002","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6040-8943","authenticated-orcid":false,"given":"Priyanka","family":"Mahajan","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Technology Guru Nanak Dev University  Amritsar India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8498-5940","authenticated-orcid":false,"given":"Prabhpreet","family":"Kaur","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Technology Guru Nanak Dev University  Amritsar India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"\u201cWorld Health Organization. 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