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Computer\u2010aided diagnosis systems have been designed and implemented to combat these issues. These systems contribute significantly to increasing the efficiency and accuracy and reducing the cost of diagnosis. Moreover, these systems must perform better so that their determined diagnosis can be more reliable. This research investigates the application of the EfficientNet architecture for the classification of hematoxylin and eosin\u2010stained breast cancer histology images provided by the ICIAR2018 dataset. Specifically, seven EfficientNets were fine\u2010tuned and evaluated on their ability to classify images into four classes: <jats:italic>normal, benign, in situ carcinoma,<\/jats:italic> and <jats:italic>invasive carcinoma<\/jats:italic>. Moreover, two standard stain normalization techniques, Reinhard and Macenko, were observed to measure the impact of stain normalization on performance. The outcome of this approach reveals that the EfficientNet\u2010B2 model yielded an accuracy and sensitivity of 98.33% using Reinhard stain normalization method on the training images and an accuracy and sensitivity of 96.67% using the Macenko stain normalization method. These satisfactory results indicate that transferring generic features from natural images to medical images through fine\u2010tuning on EfficientNets can achieve satisfactory results.<\/jats:p>","DOI":"10.1155\/2021\/5580914","type":"journal-article","created":{"date-parts":[[2021,4,10]],"date-time":"2021-04-10T05:21:03Z","timestamp":1618032063000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Classification of Hematoxylin and Eosin\u2010Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4578-0701","authenticated-orcid":false,"given":"Chanale\u00e4","family":"Munien","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2850-8645","authenticated-orcid":false,"given":"Serestina","family":"Viriri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,4,9]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21254"},{"key":"e_1_2_8_2_2","volume-title":"Breast Cancer Facts & Figures 2019-2020\u201d","author":"American Cancer Society","year":"2019"},{"key":"e_1_2_8_3_2","doi-asserted-by":"crossref","unstructured":"NawazW. 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