{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T06:49:17Z","timestamp":1784616557791,"version":"3.55.0"},"reference-count":20,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T00:00:00Z","timestamp":1743292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This work addresses the critical need for the early detection of breast cancer, a significant health concern worldwide. Using a combination of advanced deep learning and machine learning techniques, we offer a comprehensive solution to enhance breast cancer detection accuracy. By leveraging state-of-the-art convolutional neural networks (CNNs) like GoogLeNet, AlexNet, and ResNet18, alongside traditional classifiers such as k-nearest neighbors (KNN) and support vector machine (SVM), we ensure robust prediction capabilities. Our preprocessing methods significantly improve input data quality, leading to promising detection accuracies. For instance, ResNet-18 achieved impressive results, outperforming other models. Furthermore, our integration of these algorithms into a user-friendly MATLAB R2024b application ensures easy access for medical professionals, facilitating timely diagnosis and treatment. This work represents a vital step towards more effective breast cancer diagnosis, underscoring the importance of early intervention for improved patient outcomes.<\/jats:p>","DOI":"10.3390\/info16040278","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T03:25:02Z","timestamp":1743391502000},"page":"278","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["AI-Based Breast Cancer Detection System: Deep Learning and Machine Learning Approaches for Ultrasound Image Analysis"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2659-4534","authenticated-orcid":false,"given":"Amro","family":"Moursi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5359-0081","authenticated-orcid":false,"given":"Abdulrahman","family":"Aboumadi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9924-590X","authenticated-orcid":false,"given":"Uvais","family":"Qidwai","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,30]]},"reference":[{"key":"ref_1","unstructured":"Ferlay, J., Ervik, M., Lam, F., Colombet, M., Mery, L., Pi\u00f1eros, M., Znaor, A., Soerjomataram, I., and Bray, F. (2020). Global Cancer Observatory: Cancer Today, International Agency for Research on Cancer."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1056\/NEJMoa062790","article-title":"Mammographic density and the risk and detection of breast cancer","volume":"356","author":"Boyd","year":"2007","journal-title":"N. Engl. J. Med."},{"key":"ref_3","unstructured":"American Cancer Society (2025, March 05). Breast Ultrasound. Available online: https:\/\/www.cancer.org\/cancer\/breast-cancer\/screening-tests-and-early-detection\/breast-ultrasound.html."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"104863","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Gomaa","year":"2020","journal-title":"Data Brief"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104048","DOI":"10.1016\/j.pdpdt.2024.104048","article-title":"A Novel Visible and Near-Infrared Hyperspectral Imaging Platform for Automated Breast-Cancer Detection","volume":"46","author":"Youssef","year":"2024","journal-title":"Photodiagn. Photodyn. Ther."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102191","DOI":"10.1016\/j.artmed.2021.102191","article-title":"BCHisto-Net: Breast histopathological image classification by global and local feature aggregation","volume":"121","author":"Rashmi","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/38.946629","article-title":"Color transfer between images","volume":"21","author":"Reinhard","year":"2001","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_10","first-page":"4228","article-title":"Breast cancer detection and classification of histopathological images","volume":"3","author":"Singh","year":"2010","journal-title":"Int. J. Eng. Sci. Technol."},{"key":"ref_11","first-page":"316","article-title":"Multi-Class Breast Cancer Classification using Deep Learning Convolutional Neural Network","volume":"9","author":"Nawaz","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_14","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the 26th Annual Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_15","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Graves, A. (2013). Generating Sequences with Recurrent Neural Networks. arXiv.","DOI":"10.1007\/978-3-642-24797-2_3"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_18","unstructured":"Li, S., and Jain, A.K. (2007). Handbook of Face Recognition, Springer Science & Business Media."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Manning, C.D., Raghavan, P., and Sch\u00fctze, H. (2008). Introduction to Information Retrieval, Cambridge University Press.","DOI":"10.1017\/CBO9780511809071"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/4\/278\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:05:43Z","timestamp":1760029543000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/4\/278"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,30]]},"references-count":20,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["info16040278"],"URL":"https:\/\/doi.org\/10.3390\/info16040278","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,30]]}}}