{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:16:04Z","timestamp":1781763364577,"version":"3.54.5"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,1,14]],"date-time":"2024-01-14T00:00:00Z","timestamp":1705190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Faculty of Information Science and Technology- Universiti Kebangsaan Malaysia for providing Financial Support","award":["GP-2021-K008010"],"award-info":[{"award-number":["GP-2021-K008010"]}]},{"name":"Faculty of Information Science and Technology- Universiti Kebangsaan Malaysia for providing Financial Support","award":["TAP-K008010"],"award-info":[{"award-number":["TAP-K008010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Analysis of histopathological images (HIs) is crucial for detecting breast cancer (BR). However, because they vary, it is still very difficult to extract well-designed elements. Deep learning (DL) is a recent development that is used to extract high-level features. However, DL techniques continue to confront several difficult problems, such as the need for sufficient training data for DL models, which reduces the classification findings. In this study, an ensemble deep transfer convolutional neural network is presented to address this problem. The pre-trained models (ResNet50 and MobileNet) are employed to extract high-level features by freezing the front layer parameters while fine-tuning the last layers. In the proposed ensemble framework, KNN, SVM, logistic regression and neural networks are used as base classifiers. The majority vote and product approaches are used to integrate the predictions of each separate classifier. In the benchmark BreaKHis dataset, the suggested ensemble model is compared to some current approaches. It demonstrates that while the ensemble model obtains a considerable accuracy of 97.72% for the multiclass classification test, it achieves an accuracy of 99.2% for the binary task. The suggested ensemble model\u2019s effectiveness in extracting useful features for BR images is demonstrated by comparison with existing cutting-edge models.<\/jats:p>","DOI":"10.1093\/comjnl\/bxad127","type":"journal-article","created":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T06:42:00Z","timestamp":1705300920000},"page":"2126-2136","source":"Crossref","is-referenced-by-count":8,"title":["Ensemble of Deep Features for Breast Cancer Histopathological Image Classification"],"prefix":"10.1093","volume":"67","author":[{"given":"Jaffar","family":"Atwan","sequence":"first","affiliation":[{"name":"Prince Abdullah bin Ghazi Faculty of Information and Communication Technology , Al-Balqa Applied University, Salt , Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nedaa","family":"Almansour","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia , 43600 UKM Bangi, Selangor , Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Hashem Ryalat","sequence":"additional","affiliation":[{"name":"Prince Abdullah bin Ghazi Faculty of Information and Communication Technology , Al-Balqa Applied University, Salt , Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahnorbanun","family":"Sahran","sequence":"additional","affiliation":[{"name":"Medical Computing Research Group, Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia , 43600 UKM Bangi, Selangor , Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamza","family":"Aldabbas","sequence":"additional","affiliation":[{"name":"Prince Abdullah bin Ghazi Faculty of Information and Communication Technology , Al-Balqa Applied University, Salt , Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dheeb","family":"Albashish","sequence":"additional","affiliation":[{"name":"Prince Abdullah bin Ghazi Faculty of Information and Communication Technology , Al-Balqa Applied University, Salt , Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,1,14]]},"reference":[{"key":"2024062414154608600_ref1","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1158\/1055-9965.EPI-16-0858","article-title":"Global cancer in women: burden and trends","volume":"26","author":"Torre","year":"2017","journal-title":"Cancer Epidemiol. 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