{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T06:45:40Z","timestamp":1790837140144,"version":"4.1.0"},"reference-count":29,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004686","name":"Deanship of Scientific Research, King Faisal University","doi-asserted-by":"publisher","award":["KFU250685"],"award-info":[{"award-number":["KFU250685"]}],"id":[{"id":"10.13039\/501100004686","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    <jats:bold>Background\/Objective:<\/jats:bold>\n                    Breast cancer is a serious disease that has caused thousands of deaths around the world. According to the American Cancer Society, more than 40,000 women and about 600 men lost their lives due to breast cancer in 2021, and it increased to 43,700 women and 530 men until 2023.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Method<\/jats:bold>\n                    : In this paper, a modified version of ResNet-50 has been exploited to extract features from breast tissue biopsy slides, contained in the BreakHis public dataset. The standard 177 layer model is amended upto 146 layers by reducing redundant activation, normalization operations and number of convolutional filters without compromising representational capacity. As a result the computational efficiency is achieved along with reduction in learnable parameters from 23.7M to 16.8M. The features vector is extracted using novel Flea optimization Algorithm that performs exploration from a d-dimensional search space to get global features. An inter-image dissimilarity evaluation has been performed to find out class compactness and separation, demonstrating its crucial role in achieving better classification performance. The results of the proposed framework are obtained on various performance indicators including average accuracy, precision, recall, F1 score etc while the statistical analysis is made to see the reliability of the framework based on MCC, Cohen\u2019s Kappa and t-test.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    The results of the proposed method are compared with DenseNet, VGG, CNN with LSTM, Primal Dual Multi-instance SVM, Single Task CNN and Multi Task CNN and shown dominance on various performance measures. An accuracy of 99.20% was achieved at 40\u00d7 magnification, 99.62% at 100\u00d7 magnification, 99.50% at 200\u00d7 magnification, 99.34% at 400\u00d7 magnification, respectively.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusions:<\/jats:bold>\n                    The proposed approach, implemented on the real hardware, can provide an alternate to health experts in diagnosing breast cancer in the early stages.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0341848","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T18:51:49Z","timestamp":1770663109000},"page":"e0341848","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Breast cancer inter-image dissimilarity by feature optimization: An application of novel flea optimization algorithm"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5152-8409","authenticated-orcid":true,"given":"P.P.","family":"Fathimathul Rajeena","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6463-3506","authenticated-orcid":true,"given":"Muhammad","family":"Yasir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2192-3504","authenticated-orcid":true,"given":"Mona. A. 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Breast cancer detection and classification using artificial intelligence. In: 2024 1st international conference on innovative sustainable technologies for energy, mechatronics, and smart systems (ISTEMS); 2024. p. 1\u20135. https:\/\/doi.org\/10.1109\/istems60181.2024.10560257","DOI":"10.1109\/ISTEMS60181.2024.10560257"},{"issue":"1","key":"pone.0341848.ref004","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","article-title":"Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis","volume":"4","author":"R Aggarwal","year":"2021","journal-title":"NPJ Digit Med."},{"key":"pone.0341848.ref005","doi-asserted-by":"crossref","first-page":"102276","DOI":"10.1016\/j.artmed.2022.102276","article-title":"Breast cancer detection using artificial intelligence techniques: A systematic literature review","volume":"127","author":"AB Nassif","year":"2022","journal-title":"Artif Intell Med."},{"issue":"12","key":"pone.0341848.ref006","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.3390\/cancers11121901","article-title":"Parallel structure deep neural network using CNN and RNN with an attention mechanism for breast cancer histology image classification","volume":"11","author":"H Yao","year":"2019","journal-title":"Cancers (Basel)."},{"issue":"2","key":"pone.0341848.ref007","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1002\/jmri.26244","article-title":"Convolutional neural network using a breast MRI tumor dataset can predict oncotype Dx recurrence score","volume":"49","author":"R Ha","year":"2019","journal-title":"J Magn Reson Imaging."},{"key":"pone.0341848.ref008","doi-asserted-by":"crossref","unstructured":"Saadatmand S, Bretveld R, Siesling S, Tilanus-Linthorst MM. 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