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Computer\u2010aided diagnosis frameworks could reduce the number of needless biopsies and the workload of radiologists. This research aims to detect benign and malignant tumors automatically using breast ultrasound (BUS) images. Accordingly, two pretrained deep convolutional neural network (CNN) models were employed for transfer learning using BUS images like AlexNet and DenseNet201. A total of 697 BUS images containing benign and malignant tumors are preprocessed and performed classification tasks using the transfer learning\u2010based CNN models. The classification accuracy of the benign and malignant tasks is completed and achieved 92.8% accuracy using the DensNet201 model. The results thus achieved compared in state of the art using benchmark data\u2009set and concluded proposed model outperforms in accuracy from first stage breast tumor diagnosis. Finally, the proposed model could help radiologists diagnose benign and malignant tumors swiftly by screening suspected patients.<\/jats:p>","DOI":"10.1002\/jemt.24008","type":"journal-article","created":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T07:45:13Z","timestamp":1639554313000},"page":"1444-1453","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Optimizing the transfer\u2010learning with pretrained deep convolutional neural networks for first stage breast tumor diagnosis using breast ultrasound visual images"],"prefix":"10.1002","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6718-3866","authenticated-orcid":false,"given":"Tanzila","family":"Saba","sequence":"first","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics Lab, CCIS, Prince Sultan University  Riyadh 11586 Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim","family":"Abunadi","sequence":"additional","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics Lab, CCIS, Prince Sultan University  Riyadh 11586 Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0078-7849","authenticated-orcid":false,"given":"Tariq","family":"Sadad","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering International Islamic University  Islamabad 44000 Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0101-0329","authenticated-orcid":false,"given":"Amjad Rehman","family":"Khan","sequence":"additional","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics Lab, CCIS, Prince Sultan University  Riyadh 11586 Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed Ali","family":"Bahaj","sequence":"additional","affiliation":[{"name":"MIS Department, College of Business Administration Prince Sattam bin Abdulaziz University  Alkharj 11942 Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,12,15]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2474-6"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863"},{"key":"e_1_2_10_4_1","doi-asserted-by":"crossref","unstructured":"Almajalid R. 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