Refined Residual Deep Convolutional Network for Skin Lesion Classification.
Skin cancer is the most common type of cancer that affects humans and is usually diagnosed by initial clinical screening, which is followed by dermoscopic analysis. Automated classification of skin lesions is still a challenging task because of the high visual similarity between melanoma and benign...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 2; pp. 258 - 281 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2022
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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=155757669&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155757669 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2022 vid: 35 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155757669 154616980 155757669 155757669 10.1007/s10278-021-00552-0 155757669 ppf: 258 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Refined Residual Deep Convolutional Network for Skin Lesion Classification. aug: au: Hosny, Khalid M. Kassem, Mohamed A. affil: Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt sug: subj: Skin Neoplasms Diagnosis Skin Neoplasms Classification Neural Networks (Computer) Diagnosis, Computer Assisted Image Processing, Computer Assisted Methods Machine Learning Death Melanoma Deep Learning ab: Skin cancer is the most common type of cancer that affects humans and is usually diagnosed by initial clinical screening, which is followed by dermoscopic analysis. Automated classification of skin lesions is still a challenging task because of the high visual similarity between melanoma and benign lesions. This paper proposes a new residual deep convolutional neural network (RDCNN) for skin lesions diagnosis. The proposed neural network is trained and tested using six well-known skin cancer datasets, PH2, DermIS and Quest, MED-NODE, ISIC2016, ISIC2017, and ISIC2018. Three different experiments are carried out to measure the performance of the proposed RDCNN. In the first experiment, the proposed RDCNN is trained and tested using the original dataset images without any pre-processing or segmentation. In the second experiment, the proposed RDCNN is tested using segmented images. Finally, the utilized trained model in the second experiment is saved and reused in the third experiment as a pre-trained model. Then, it is trained again using a different dataset. The proposed RDCNN shows significant high performance and outperforms the existing deep convolutional networks. 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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