{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T09:10:37Z","timestamp":1778749837496,"version":"3.51.4"},"reference-count":35,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2022,12,4]],"date-time":"2022-12-04T00:00:00Z","timestamp":1670112000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2022,12,4]],"date-time":"2022-12-04T00:00:00Z","timestamp":1670112000000},"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":["Expert Systems"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>It has always been difficult and challenging to quantify the breast imaging reporting and data system (BI\u2010RADS) criteria into several categories. Automatic quantitation can assist clinicians in the early diagnosis and treatment eventually reducing the mortality rate. As a result, in the recent years, early BC diagnosis methods based on pathological breast images have been in high demand.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>We propose a computer\u2010aided diagnosis (CAD) system that combines the transfer learning approach with meta\u2010heuristic optimization, and machine learning to classify BI\u2010RADS breast masses categories within levels 3 and 4. Transfer learning technique ResNet\u201018 is used for high\u2010level feature extraction. The clinically important features are then chosen using a modified feature selection technique based on the Hyper Learning Binary DragonFly Algorithm (M\u2010HLBDA). Finally, a Fine K\u2010nearest neighbour (KNN) is employed for classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>A series of mammography breast mass images from the curated breast imaging subset of DDSM (CBIS\u2010DDSM) are evaluated in order to categorize within BI\u2010RADS levels 3 and 4. Experimental findings demonstrated M\u2010HLBDA capability to identify the optimal feature subset, which minimizes the number of selected features and maximizes the classification. Our system attained classification accuracy of 87.5%, Sensitivity of 88.8%, Specificity of 86.5%, and AUC of 0.82 using KNN on selected features using M\u2010HLBDA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our model can annotate and classify BI\u2010RADS levels 3 and 4 with better classification accuracy, and it may be used as an automated system to help radiologists.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/exsy.13200","type":"journal-article","created":{"date-parts":[[2022,12,4]],"date-time":"2022-12-04T22:50:30Z","timestamp":1670194230000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Combining modified hyper learning binary dragonfly algorithm and deep learning for\n                    <scp>BI\u2010RADS<\/scp>\n                    classification of breast masses in mammograms"],"prefix":"10.1111","volume":"43","author":[{"given":"Priyanka","family":"Khanna","sequence":"first","affiliation":[{"name":"Department of Information Technology National Institute of Technology Raipur  Raipur India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mridu","family":"Sahu","sequence":"additional","affiliation":[{"name":"Department of Information Technology National Institute of Technology Raipur  Raipur India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5052-9768","authenticated-orcid":false,"given":"Bikesh Kumar","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering National Institute of Technology Raipur  Raipur India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3259-8874","authenticated-orcid":false,"given":"Vikrant","family":"Bhateja","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering Shri Ramswaroop Memorial College of Engineering and Management  Lucknow India"},{"name":"Dr. A.P.J. Abdul Kalam Technical University  Lucknow India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,12,4]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1428-9"},{"key":"e_1_2_10_3_1","volume-title":"Bi\u2010Rads committee, ACR BI\u2010RADS atlas: Breast imaging reporting and data system","author":"American College of Radiology","year":"2013"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.04.065"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1453-8"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.5336\/biostatic.2019-64754"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.431077"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/7695207"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_78"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2006.05.002"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2016.0244"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2018.8451510"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCE.2019.2923926"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2004.1403858"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2746879"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1388-0"},{"key":"e_1_2_10_17_1","doi-asserted-by":"crossref","unstructured":"He K. 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Liu W. Jia Y. Sermanet P. Reed S. Anguelov D. \u2026Rabinovich A.(2015).Going deeper with convolutions. 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