dc.contributor.author | Melit Devassy, Binu | |
dc.contributor.author | Yildirim Yayilgan, Sule | |
dc.contributor.author | Hardeberg, Jon Yngve | |
dc.date.accessioned | 2019-03-22T08:35:51Z | |
dc.date.available | 2019-03-22T08:35:51Z | |
dc.date.created | 2018-09-12T14:19:21Z | |
dc.date.issued | 2018 | |
dc.identifier.issn | 2194-5357 | |
dc.identifier.uri | http://hdl.handle.net/11250/2591220 | |
dc.description.abstract | Melanoma is the deadliest form of skin cancer and it is the most rapidly spreading cancer in the world. An earlier detection of this kind of cancer is curable; hence, earlier detection of melanoma is pre-eminent. Because of this fact, a lot of research is being done in this area especially in automatic detection of melanoma. In this paper, we are proposing an automatic melanoma detection system which utilizes a combination of deep and hand-crafted features. We analyzed the impact of using a simpler and standard hand-crafted feature, in place of complex usual hand-crafted features e.g. shape, texture, diameter, or some custom features. We used a convolutional neural network (CNN) known as deep residual network (ResNet) to extract the deep features and utilized the scale invariant feature descriptor (SIFT) as the hand-crafted feature. The experiments revealed that combining SIFT did not improve the accuracy of the system however, we obtained higher accuracy than state-of-the-art methods with our deep only solution. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Springer Verlag | nb_NO |
dc.title | The Impact of Replacing Complex Hand-Crafted Features with Standard Features for Melanoma Classification using Both Hand-Crafted and Deep Features | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.volume | 868 | nb_NO |
dc.source.journal | Advances in Intelligent Systems and Computing | nb_NO |
dc.identifier.doi | 10.1007/978-3-030-01054-6_10 | |
dc.identifier.cristin | 1608917 | |
dc.relation.project | Norges forskningsråd: 247689 | nb_NO |
dc.description.localcode | This is a post-peer-review, pre-copyedit version of an article published in [Advances in Intelligent Systems and Computing] Locked until 9.11.2019 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-01054-6_10 | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitcode | 194,63,30,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.unitname | Institutt for informasjonssikkerhet og kommunikasjonsteknologi | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |