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In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.<\/jats:p>","DOI":"10.1371\/journal.pone.0262349","type":"journal-article","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T18:52:00Z","timestamp":1642186320000},"page":"e0262349","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":108,"title":["Deep learning model for fully automated breast cancer detection system from thermograms"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6877-9610","authenticated-orcid":true,"given":"Esraa A.","family":"Mohamed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6571-9807","authenticated-orcid":true,"given":"Essam A.","family":"Rashed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4065-4191","authenticated-orcid":true,"given":"Tarek","family":"Gaber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar","family":"Karam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"key":"pone.0262349.ref001","doi-asserted-by":"crossref","unstructured":"Ali M, Sayed G, Gaber T, Hassanien A, Snasel V and Silva L. Detection of breast abnormalities of thermograms based on a new segmentation method. In: Proceedings of the Federated Conference on Computer Science and Information Systems; \u0141\u00f3d\u017a, Poland. 2015.","DOI":"10.15439\/2015F318"},{"key":"pone.0262349.ref002","unstructured":"Globocan 2018. All Cancer. International Agency for Research on Cancer WHO (2019), [Online]. Available: http:\/\/gco.iarc.fr\/today\/data\/factsheets\/cancers\/39-All-cancers-fact-sheet.pdf. 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