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DC poleHodnotaJazyk
dc.contributor.authorJunayed, Masum Shah
dc.contributor.authorAnjum, Nipa
dc.contributor.authorNoman, Abu
dc.contributor.authorIslam, Baharul
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-08-31T06:35:35Z
dc.date.available2021-08-31T06:35:35Z
dc.date.issued2021
dc.identifier.citationWSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 71-80.en
dc.identifier.isbn978-80-86943-34-3
dc.identifier.issn2464-4617
dc.identifier.issn2464–4625(CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/45011
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectrakovina kůžecs
dc.subjectdatasetcs
dc.subjectaugmentace datcs
dc.subjectkonvoluční neurální síťcs
dc.subjectlékařský obrazcs
dc.subjectpočítačové viděnícs
dc.titleA Deep CNN Model for Skin Cancer Detection and Classificationen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedSkin cancer is one of the most dangerous types of cancers that affect millions of people every year. The detection ofskin cancer in the early stages is an expensive and challenging process. In recent studies, machine learning-basedmethods help dermatologists in classifying medical images. This paper proposes a deep learning-based modelto detect and classify skin cancer using the concept of deep Convolution Neural Network (CNN). Initially, wecollected a dataset that includes four skin cancer image data before applying them in augmentation techniques toincrease the accumulated dataset size. Then, we designed a deep CNN model to train our dataset. On the test data,our model receives 95.98% accuracy that exceeds the two pre-train models, GoogleNet by 1.76% and MobileNetby 1.12%, respectively. The proposed deep CNN model also beats other contemporaneous models while beingcomputationally comparable.en
dc.subject.translatedskin canceren
dc.subject.translateddataseten
dc.subject.translateddata augmentationen
dc.subject.translateddeep CNNen
dc.subject.translatedmedical imageen
dc.subject.translatedcomputer visionen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2021.3101.8
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2021: Full Papers Proceedings

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