{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T17:28:16Z","timestamp":1790184496117,"version":"4.1.0"},"reference-count":44,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T00:00:00Z","timestamp":1621468800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Computer aided diagnosis (<jats:bold>CAD<\/jats:bold>) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (<jats:bold>FL<\/jats:bold>) and deep learning (<jats:bold>DL<\/jats:bold>) for automatic semantic segmentation (<jats:bold>SS<\/jats:bold>) of tumors in breast ultrasound (<jats:bold>BUS<\/jats:bold>) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (<jats:bold>CNN<\/jats:bold>) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: <jats:bold>batch<\/jats:bold> and <jats:bold>one by one<\/jats:bold> image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (<jats:bold>GA<\/jats:bold>), mean Jaccard Index (mean intersection over union (<jats:bold>IoU<\/jats:bold>)), and mean BF (Boundary F1) Score. <jats:bold>In the batch processing mode<\/jats:bold>: quantitative metrics\u2019 average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: <jats:bold>95.45<\/jats:bold>% GA instead of <jats:bold>86.08<\/jats:bold>% without applying fuzzy preprocessing step, <jats:bold>78.70<\/jats:bold>% mean IoU instead of <jats:bold>49.61<\/jats:bold>%, and <jats:bold>68.08<\/jats:bold>% mean BF score instead of <jats:bold>42.63<\/jats:bold>%. Moreover, the resulted segmented images could show tumors\u2019 regions more accurate than with only CNN based SS. <jats:bold>While, in one by one image processing mode:<\/jats:bold> there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation\u2019s efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (<jats:bold>ROI<\/jats:bold>) in any batch of digital images. A modified small dataset is available: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/www.kaggle.com\/mohammedtgadallah\/mt-small-dataset\" xlink:type=\"simple\">https:\/\/www.kaggle.com\/mohammedtgadallah\/mt-small-dataset<\/jats:ext-link> (S1 Data).<\/jats:p>","DOI":"10.1371\/journal.pone.0251899","type":"journal-article","created":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T18:05:04Z","timestamp":1621533904000},"page":"e0251899","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":74,"title":["Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning\u2014A feasibility study"],"prefix":"10.1371","volume":"16","author":[{"given":"Samir M.","family":"Badawy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abd El-Naser A.","family":"Mohamed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alaa A.","family":"Hefnawy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hassan E.","family":"Zidan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9353-3986","authenticated-orcid":true,"given":"Mohammed T.","family":"GadAllah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghada M.","family":"El-Banby","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,5,20]]},"reference":[{"issue":"10","key":"pone.0251899.ref001","article-title":"Breast Cancer Detection with Mammogram Segmentation: A Qualitative Study","volume":"8","author":"SM Badawy","year":"2017","journal-title":"IJACSA"},{"key":"pone.0251899.ref002","unstructured":"American Cancer Society. 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