{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T17:54:05Z","timestamp":1767117245252,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T00:00:00Z","timestamp":1602547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013317","name":"Key Research and Development Plan of Shanxi Province","doi-asserted-by":"publisher","award":["201703D111023","201703D111027"],"award-info":[{"award-number":["201703D111023","201703D111027"]}],"id":[{"id":"10.13039\/501100013317","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanxi International Cooperation Project","award":["201803D421039"],"award-info":[{"award-number":["201803D421039"]}]},{"name":"Hundred Talents Programme of Shanxi","award":["2018"],"award-info":[{"award-number":["2018"]}]},{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["201801D121144"],"award-info":[{"award-number":["201801D121144"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discriminant loss in dermoscopy images. Deep convolutional neural network is trained under the joint supervision of cross entropy loss and covariance discriminant loss, rectifying the model outputs and the extracted features simultaneously. Specifically, we design an embedding loss, namely covariance discriminant loss, which takes the first and second distance into account simultaneously for providing more constraints. By constraining the distance between hard samples and minority class center, the deep features of melanoma and non-melanoma can be separated effectively. To mine the hard samples, we also design the corresponding algorithm. Further, we analyze the relationship between the proposed loss and other losses. On the International Symposium on Biomedical Imaging (ISBI) 2018 Skin Lesion Analysis dataset, the two schemes in the proposed method can yield a sensitivity of 0.942 and 0.917, respectively. The comprehensive results have demonstrated the efficacy of the designed embedding loss and the proposed methodology.<\/jats:p>","DOI":"10.3390\/s20205786","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T21:24:39Z","timestamp":1602710679000},"page":"5786","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1559-6022","authenticated-orcid":false,"given":"Lei","family":"Guo","sequence":"first","affiliation":[{"name":"College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Shanxi Key Laboratory of Advanced Control and Intelligent Information System, School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinying","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6116-3194","authenticated-orcid":false,"given":"Jinchang","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"177","DOI":"10.3322\/caac.21395","article-title":"Colorectal cancer statistics, 2017","volume":"67","author":"Siegel","year":"2017","journal-title":"CA Cancer J. 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