{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T06:53:43Z","timestamp":1790924023212,"version":"4.1.0"},"reference-count":34,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Biophotonics"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Automated, as well as accurate classification with breast cancer histological images, was crucial for medical applications because of detecting malignant tumors via histopathological images. In this work create a Fourier ptychographic (FP) and deep learning using breast cancer histopathological image classification. Here the FP method used in the process begins with such a random guess that builds a high\u2010resolution complex hologram, subsequently uses iterative retrieval using FP constraints to stitch around each other low\u2010resolution multi\u2010view means of production owned from either the hologram's high\u2010resolution hologram's elemental images captured via integral imaging. Next, the feature extraction process includes entropy, geometrical features, and textural features. The entropy\u2010based normalization is used to optimize the features. Finally, it attains the classification process of the proposed ENDNN classifies the breast cancer images into normal or abnormal. The experimental outcomes demonstrate that our presented technique overtakes the traditional techniques.\n                    <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/jbio202300194-gra-0001.png\" xlink:title=\"image\"\/>\n                  <\/jats:p>","DOI":"10.1002\/jbio.202300194","type":"journal-article","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T00:20:01Z","timestamp":1686356401000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fourier ptychographic and deep learning using breast cancer histopathological image classification"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4511-6421","authenticated-orcid":false,"given":"Leena","family":"Thomas","sequence":"first","affiliation":[{"name":"Department of Electronics &amp; Communication Engineering Sree Chitra Thirunal College of Engineering  Thiruvananthapuram Kerala India"},{"name":"APJ Abdul Kalam Technological University  Kerala India"},{"name":"College of Engineering Kallooppara  Pathanamthitta Kerala India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. K.","family":"Sheeja","sequence":"additional","affiliation":[{"name":"Department of Electronics &amp; Communication Engineering Sree Chitra Thirunal College of Engineering  Thiruvananthapuram Kerala India"},{"name":"APJ Abdul Kalam Technological University  Kerala India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"e_1_2_8_2_1","volume-title":"International Agency for Research on Cancer","author":"Lakhani S. 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