{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T09:56:06Z","timestamp":1790848566128,"version":"4.1.0"},"reference-count":35,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Microscopy Res &amp; Technique"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Uterine cancer is one of the most common malignancies in women, and early detection is vital for improving clinical outcomes and enhancing women's well\u2010being. However, subtle tissue variations make manual diagnosis subjective and time\u2010consuming. This study presents a transformer\u2010enhanced deep convolutional neural network (TE\u2010DCNN) for automated classification and segmentation of uterine histopathological images. The architecture integrates convolutional layers for local feature extraction with transformer modules for global contextual learning, supported by dual channel\u2013spatial attention (SA) to highlight diagnostically critical regions. Evaluated on four classes\u2014Normal Endometrium, Endometrial Hyperplasia, Endometrial Polyp, and Endometrial Adenocarcinoma, the model achieved 99% accuracy, 0.97 sensitivity, and 0.98 specificity, outperforming CNN and vision transformer (ViT) baselines. Visual segmentation confirmed precise localization of malignant areas, enhancing interpretability. The proposed TE\u2010DCNN offers a fast, explainable, and reliable tool for early uterine cancer diagnosis and can be extended to other gynecological imaging tasks.<\/jats:p>","DOI":"10.1002\/jemt.70176","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T09:05:31Z","timestamp":1790845531000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transformer\u2010Enhanced Deep\n                    <scp>CNN<\/scp>\n                    for Early Detection of Uterine Cancer in Histopathological Images"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5236-1671","authenticated-orcid":false,"given":"P. M.","family":"Lavanya","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering Sathyabama Institute of Science and Technology (Deemed To Be University) Jeppiaar Nagar  Chennai Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9616-3871","authenticated-orcid":false,"given":"Mahesh Muthulakshmi","family":"R","sequence":"additional","affiliation":[{"name":"Institute of Computer Science and Engineering, Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences  Chennai Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3083-0553","authenticated-orcid":false,"given":"P.","family":"Baraneedharan","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering SRM Institute of Science &amp; Technology (SRMIST), Tiruchirappalli Campus  Tiruchirappalli Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"Rasi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering (Cyber Security) Karpagam College of Engineering  Coimbatore Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4892-5809","authenticated-orcid":false,"given":"K. Amuthambigaiyin","family":"Sundari","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering RVS Technical Campus  Coimbatore Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0652-0501","authenticated-orcid":false,"given":"D.","family":"Sheema","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Sathyabama Institute of Science and Technology (Deemed To Be University) Jeppiaar Nagar  Chennai Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-00906-6","article-title":"Radiomic\u2010Based Model to Predict Myometrial Invasion on Ultrasound: Methodology and Validation","volume":"15","author":"Arezzo F.","year":"2025","journal-title":"Scientific 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