Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet.
Melanoma is deadly skin cancer. There is a high similarity between different kinds of skin lesions, which lead to incorrect classification. Accurate classification of a skin lesion in its early stages saves human life. In this paper, a highly accurate method proposed for the skin lesion classificati...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 5; pp. 1325 - 1335 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2020
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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=146532226&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146532226 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2020 vid: 33 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146532226 144442533 146532226 146532226 10.1007/s10278-020-00371-9 146532226 ppf: 1325 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet. aug: au: Hosny, Khalid M. Kassem, Mohamed A. Fouad, Mohamed M. affil: Department of Information Technology, Faculty of Computers and Informatics, Zagazig, University, Zagazig 44519, Egypt sug: subj: Skin Injuries Skin Neoplasms Classification Human Melanoma Classification Carcinoma, Basal Cell Classification Keratosis, Actinic Classification Neoplasms, Fibrous Tissue Classification Sensitivity and Specificity ab: Melanoma is deadly skin cancer. There is a high similarity between different kinds of skin lesions, which lead to incorrect classification. Accurate classification of a skin lesion in its early stages saves human life. In this paper, a highly accurate method proposed for the skin lesion classification process. The proposed method utilized transfer learning with pre-trained AlexNet. The parameters of the original model used as initial values, where we randomly initialize the weights of the last three replaced layers. The proposed method was tested using the most recent public dataset, ISIC 2018. Based on the obtained results, we could say that the proposed method achieved a great success where it accurately classifies the skin lesions into seven classes. These classes are melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, and vascular lesion. The achieved percentages are 98.70%, 95.60%, 99.27%, and 95.06% for accuracy, sensitivity, specificity, and precision, respectively. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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