{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:21:38Z","timestamp":1792790498741,"version":"4.5.0"},"reference-count":40,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T00:00:00Z","timestamp":1654732800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,6,9]]},"abstract":"<jats:p>In the world, in the past recent five years, breast cancer is diagnosed about 7.8 million women\u2019s and making it the most widespread cancer, and it is the second major reason for women\u2019s death. So, early prevention and diagnosis systems of breast cancer could be more helpful and significant. Neural networks can extract multiple features automatically and perform predictions on breast cancer. There is a need for several labeled images to train neural networks which is a nonconventional method for some types of data images such as breast magnetic resonance imaging (MRI) images. So, there is only one significant solution for this query is to apply fine-tuning in the neural network. In this paper, we proposed a fine-tuning model using AlexNet in the neural network to extract features from breast cancer images for training purposes. So, in the proposed model, we updated the first and last three layers of AlexNet to detect the normal and abnormal regions of breast cancer. The proposed model is more efficient and significant because, during the training and testing process, the proposed model achieves higher accuracy 98.44% and 98.1% of training and testing, respectively. So, this study shows that the use of fine-tuning in the neural network can detect breast cancer using MRI images and train a neural network classifier by feature extraction using the proposed model is faster and more efficient.<\/jats:p>","DOI":"10.1155\/2022\/5918686","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T01:35:23Z","timestamp":1654824923000},"page":"1-9","source":"Crossref","is-referenced-by-count":69,"title":["Breast Cancer Prediction Empowered with Fine-Tuning"],"prefix":"10.1155","volume":"2022","author":[{"given":"Muhammad Umar","family":"Nasir","sequence":"first","affiliation":[{"name":"Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taher M.","family":"Ghazal","sequence":"additional","affiliation":[{"name":"School of Information Technology, Skyline University College, Sharjah 1797, UAE"},{"name":"Network and Communication Technology Lab, Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6799-0390","authenticated-orcid":true,"given":"Muhammad Adnan","family":"Khan","sequence":"additional","affiliation":[{"name":"Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, Gyeonggido 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Zubair","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Riphah International University, Islamabad 45000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6696-277X","authenticated-orcid":true,"given":"Atta-ur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rashad","family":"Ahmed","sequence":"additional","affiliation":[{"name":"ICS Department, King Fahd University of Petroleum and Minerls, Dhahran 31261, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1353-4138","authenticated-orcid":true,"given":"Hussam Al","family":"Hamadi","sequence":"additional","affiliation":[{"name":"Center for Cyber Physical Systems, Khalifa University, Abu Dhabi 127788, UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chan Yeob","family":"Yeun","sequence":"additional","affiliation":[{"name":"Center for Cyber Physical Systems, Khalifa University, Abu Dhabi 127788, UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2009.1993"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1117\/12.2081576"},{"issue":"1","key":"3","doi-asserted-by":"crossref","first-page":"116","DOI":"10.4048\/jbc.2017.20.1.116","article-title":"Magnetic resonance imaging features of adenosis in the breast","volume":"20","author":"M. 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