Rethinking Skin Lesion Segmentation in a Convolutional Classifier.
Melanoma is a fatal form of skin cancer when left undiagnosed. Computer-aided diagnosis systems powered by convolutional neural networks (CNNs) can improve diagnostic accuracy and save lives. CNNs have been successfully used in both skin lesion segmentation and classification. For reasons heretofore...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 4; pp. 435 - 441 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131471418&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131471418 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2018 vid: 31 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131471418 131471418 131471418 10.1007/s10278-017-0026-y 131471418 ppf: 435 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Rethinking Skin Lesion Segmentation in a Convolutional Classifier. aug: au: Burdick, Jack Marques, Oge Weinthal, Janet Furht, Borko affil: Florida Atlantic University, Boca Raton, FL, USA sug: subj: Skin Diseases Classification Diagnosis, Computer Assisted Neural Networks (Computer) Methods Melanoma Diagnosis Skin Artifacts ab: Melanoma is a fatal form of skin cancer when left undiagnosed. Computer-aided diagnosis systems powered by convolutional neural networks (CNNs) can improve diagnostic accuracy and save lives. CNNs have been successfully used in both skin lesion segmentation and classification. For reasons heretofore unclear, previous works have found image segmentation to be, conflictingly, both detrimental and beneficial to skin lesion classification. We investigate the effect of expanding the segmentation border to include pixels surrounding the target lesion. Ostensibly, segmenting a target skin lesion will remove inessential information, non-lesion skin, and artifacts to aid in classification. Our results indicate that segmentation border enlargement produces, to a certain degree, better results across all metrics of interest when using a convolutional based classifier built using the transfer learning paradigm. Consequently, preprocessing methods which produce borders larger than the actual lesion can potentially improve classifier performance, more than both perfect segmentation, using dermatologist created ground truth masks, and no segmentation altogether. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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