Multiscale Feature Fusion for Skin Lesion Classification.
Skin cancer has a high mortality rate, and early detection can greatly reduce patient mortality. Convolutional neural network (CNN) has been widely applied in the field of computer-aided diagnosis. To improve the ability of convolutional neural networks to accurately classify skin lesions, we propos...
| Published in: | BioMed Research International pp. 1 - 16 |
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| Main Authors: | , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
1/5/2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161163149&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161163149 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 1/5/2023 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 161163149 161163149 161163149 10.1155/2023/5146543 161163149 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multiscale Feature Fusion for Skin Lesion Classification. aug: au: Wang, Gang Yan, Pu Tang, Qingwei Yang, Lijuan Chen, Jie affil: College of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230000, China sug: subj: Skin Neoplasms Diagnosis Skin Diseases Classification Neural Networks (Computer) Methods Diagnosis, Computer Assisted Methods Human Models, Statistical Early Detection of Cancer Skin Neoplasms Mortality Image Processing, Computer Assisted Evaluation Research ab: Skin cancer has a high mortality rate, and early detection can greatly reduce patient mortality. Convolutional neural network (CNN) has been widely applied in the field of computer-aided diagnosis. To improve the ability of convolutional neural networks to accurately classify skin lesions, we propose a multiscale feature fusion model for skin lesion classification. We use a two-stream network, which are a densely connected network (DenseNet-121) and improved visual geometry group network (VGG-16). In the feature fusion module, we construct multireceptive fields to obtain multiscale pathological information and use generalized mean pooling (GeM pooling) to reduce the spatial dimensionality of lesion features. Finally, we built and tested a system with the developed skin lesion classification model. The experiments were performed on the dataset ISIC2018, which can achieve a good classification performance with a test accuracy of 91.24% and macroaverages of 95%. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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