Skin Cancer Classification Using Deep Spiking Neural Network.
Skin cancer is one of the primary causes of death globally, and experts diagnose it by visual inspection, which can be inaccurate. The need for developing a computer-aided method to aid dermatologists in diagnosing skin cancer is highlighted by the fact that early identification can lower the number...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 3; pp. 1137 - 1148 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2023
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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=164473103&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164473103 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164473103 161445879 164473103 164473103 10.1007/s10278-023-00776-2 164473103 ppf: 1137 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Skin Cancer Classification Using Deep Spiking Neural Network. aug: au: Qasim Gilani, Syed Syed, Tehreem Umair, Muhammad Marques, Oge affil: Department of Electrical Engineering and Computer Science, Florida Atlantic University, 33431, Boca Raton, FL, USA sug: subj: Skin Neoplasms Classification Deep Learning Neural Networks (Computer) Image Processing, Computer Assisted Inspection (Clinical) Skin Neoplasms Mortality Early Detection of Cancer Melanoma Sensitivity and Specificity Skin Neoplasms Diagnosis ab: Skin cancer is one of the primary causes of death globally, and experts diagnose it by visual inspection, which can be inaccurate. The need for developing a computer-aided method to aid dermatologists in diagnosing skin cancer is highlighted by the fact that early identification can lower the number of deaths caused by skin malignancies. Among computer-aided techniques, deep learning is the most popular for identifying cancer from skin lesion images. Due to their power-efficient behavior, spiking neural networks are attractive deep neural networks for hardware implementation. We employed deep spiking neural networks using the surrogate gradient descent method to classify 3670 melanoma and 3323 non-melanoma images from the ISIC 2019 dataset. We achieved an accuracy of 89.57% and an F1 score of 90.07% using the proposed spiking VGG-13 model, which is higher than the VGG-13 and AlexNet using less trainable parameters. pubtype: Academic Journal doctype: equations & formulas pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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