Automatic Diagnosis of Melanoma Based on EfficientNet and Patch Strategy
Melanoma is a fatal skin disease, and there are many challenging tasks in the detection of melanoma through neural network at this stage. We propose a new method for melanoma diagnosis based on EfficientNet and patch strategy. The diagnosis method has three stages of operation. First, Cyclegan is ap...
Saved in:
| Published in | International journal of computational intelligence systems Vol. 16; no. 1; pp. 1 - 18 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
19.05.2023
Springer Nature B.V Springer |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1875-6883 1875-6891 1875-6883 |
| DOI | 10.1007/s44196-023-00246-1 |
Cover
| Abstract | Melanoma is a fatal skin disease, and there are many challenging tasks in the detection of melanoma through neural network at this stage. We propose a new method for melanoma diagnosis based on EfficientNet and patch strategy. The diagnosis method has three stages of operation. First, Cyclegan is applied offline to synthesize the under-represented category samples from the over-represented category samples, and the original image and the synthesized samples are combined into a new training set to complete the conditional image synthesis task; second, we creatively propose the patch strategy and implement the patch algorithm, and apply the patch strategy offline to obtain the patch image of the newly merged training set; finally, the newly merged training set and the obtained patch image are sent to the classification network. Where, the model we proposed is composed of three parts: Basic Convolutional Neural Network, Auxiliary Convolutional Neural Network, and Fusion Convolutional Neural Network, and applies a weighted integration strategy. We evaluated the proposed method on the ISIC 2016 Skin Injury Challenge classification dataset. Experiments show that the patch strategy plays an important role in the field of melanoma classification, and the melanoma detection method proposed in this paper obtains an accuracy of 0.852 and an AUC value of 0.854 on the test set. This method can focus the attention of the classification network on the meaningful area of the skin lesion image through manual intervention, and can effectively solve the problem of category imbalance, thereby improving the performance of skin lesion classification. |
|---|---|
| AbstractList | Melanoma is a fatal skin disease, and there are many challenging tasks in the detection of melanoma through neural network at this stage. We propose a new method for melanoma diagnosis based on EfficientNet and patch strategy. The diagnosis method has three stages of operation. First, Cyclegan is applied offline to synthesize the under-represented category samples from the over-represented category samples, and the original image and the synthesized samples are combined into a new training set to complete the conditional image synthesis task; second, we creatively propose the patch strategy and implement the patch algorithm, and apply the patch strategy offline to obtain the patch image of the newly merged training set; finally, the newly merged training set and the obtained patch image are sent to the classification network. Where, the model we proposed is composed of three parts: Basic Convolutional Neural Network, Auxiliary Convolutional Neural Network, and Fusion Convolutional Neural Network, and applies a weighted integration strategy. We evaluated the proposed method on the ISIC 2016 Skin Injury Challenge classification dataset. Experiments show that the patch strategy plays an important role in the field of melanoma classification, and the melanoma detection method proposed in this paper obtains an accuracy of 0.852 and an AUC value of 0.854 on the test set. This method can focus the attention of the classification network on the meaningful area of the skin lesion image through manual intervention, and can effectively solve the problem of category imbalance, thereby improving the performance of skin lesion classification. Abstract Melanoma is a fatal skin disease, and there are many challenging tasks in the detection of melanoma through neural network at this stage. We propose a new method for melanoma diagnosis based on EfficientNet and patch strategy. The diagnosis method has three stages of operation. First, Cyclegan is applied offline to synthesize the under-represented category samples from the over-represented category samples, and the original image and the synthesized samples are combined into a new training set to complete the conditional image synthesis task; second, we creatively propose the patch strategy and implement the patch algorithm, and apply the patch strategy offline to obtain the patch image of the newly merged training set; finally, the newly merged training set and the obtained patch image are sent to the classification network. Where, the model we proposed is composed of three parts: Basic Convolutional Neural Network, Auxiliary Convolutional Neural Network, and Fusion Convolutional Neural Network, and applies a weighted integration strategy. We evaluated the proposed method on the ISIC 2016 Skin Injury Challenge classification dataset. Experiments show that the patch strategy plays an important role in the field of melanoma classification, and the melanoma detection method proposed in this paper obtains an accuracy of 0.852 and an AUC value of 0.854 on the test set. This method can focus the attention of the classification network on the meaningful area of the skin lesion image through manual intervention, and can effectively solve the problem of category imbalance, thereby improving the performance of skin lesion classification. |
| ArticleNumber | 87 |
| Author | Zou, Qingxu Cheng, Jinyong Liang, Zhenlu |
| Author_xml | – sequence: 1 givenname: Qingxu surname: Zou fullname: Zou, Qingxu organization: School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences) – sequence: 2 givenname: Jinyong surname: Cheng fullname: Cheng, Jinyong organization: School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences) – sequence: 3 givenname: Zhenlu orcidid: 0000-0001-9960-0839 surname: Liang fullname: Liang, Zhenlu email: lzl@qlu.edu.cn organization: School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences) |
| BookMark | eNqNkU1vFSEYhYmpibX2D7gicT368nFnmGWttW1StUl1Td7h48rNFK7Ajbn_vthpauOicQMEznPgHF6Tg5iiI-Qtg_cMYPhQpGRj3wEXHQCXfcdekEOmhlXXKyUOnqxfkeNSNtBUTAJIeUguTnY13WINhn4KuI6phEKTp1_cjLEd0I9YnKUp0jPvgwku1q-uUoyWXmM1P-lNzVjdev-GvPQ4F3f8MB-RH5_Pvp9edFffzi9PT646I_lYO8O5E75vA_ZiEtasrLUeLQyiB4vcy5UCjoMS08SN9cYJi6hQummUHiZxRC4XX5two7c53GLe64RB32-kvNaYW5zZaQs4rhzrgQ9OWoXI1ChNq2kyk_JibF5i8drFLe5_4zw_GjLQf7rVS7e6Qfq-W80a9W6htjn92rlS9SbtcmyhtYCBD1ICk03FF5XJqZTs_P9Zq38gE2r7nBRby2F-Hn3IUto9ce3y31c9Q90BHnKrrA |
| CitedBy_id | crossref_primary_10_1049_ipr2_13219 |
| Cites_doi | 10.1088/0031-9155/60/9/3415 10.1016/j.cosrev.2021.100379 10.1109/TMI.2019.2893944 10.1109/ISBI.2018.8363547 10.1016/j.compmedimag.2018.10.007 10.1007/978-3-030-01201-4_33 10.1109/ICCV.2017.244 10.1016/j.cmpb.2019.07.005 10.1162/089976600300015312 10.1109/JBHI.2020.2977013 10.1038/s41598-021-84593-z 10.1088/1361-6560/ab86d3 10.3390/s20247080 10.1109/BIBM49941.2020.9313451 10.3390/e22040484 10.1186/s12859-021-04082-y 10.1016/j.artmed.2020.101938 10.1001/archderm.1995.01690150050011 10.1007/978-3-319-68127-6_2 10.1186/s13640-019-0465-0 10.1155/2021/6671498 10.1007/978-3-030-20351-1_62 10.1109/42.918473 10.1049/rsn2.12089 10.1109/EMBC.2018.8512488 10.1016/j.media.2016.06.023 10.1016/j.media.2019.02.010 10.1016/j.cmpb.2021.106447 10.1016/j.compbiomed.2021.104540 10.1109/ACCESS.2021.3065701 10.1109/JBHI.2018.2824327 10.1038/nature21056 10.1109/CVPR.2017.632 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2023 The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2023 – notice: The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D ADTOC UNPAY DOA |
| DOI | 10.1007/s44196-023-00246-1 |
| DatabaseName | Springer Nature Open Access Journals CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Open Access Full Text url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 1875-6883 |
| EndPage | 18 |
| ExternalDocumentID | oai_doaj_org_article_d0a95e16027e4d8aa1894c023bcb8f39 10.1007/s44196-023-00246-1 10_1007_s44196_023_00246_1 |
| GrantInformation_xml | – fundername: Natural Science Foundation of Shandong Province grantid: 23170807 funderid: http://dx.doi.org/10.13039/501100007129 – fundername: Key Research and Development Project of Shandong Province grantid: 2019JZZY020124 |
| GroupedDBID | 0R~ 4.4 5GY AAFWJ AAJSJ AAKKN AAYZJ ABEEZ ABFIM ACACY ACGFS ACULB ADBBV ADCVX AENEX AFGXO AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS ARAPS ARCSS AVBZW BCNDV BENPR BGLVJ C24 C6C CS3 DU5 EBLON EBS EJD GROUPED_DOAJ GTTXZ HCIFZ HZ~ J~4 K7- O9- OK1 PIMPY RSV SOJ TFW TR2 AASML AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D ADMSI ADTOC AHDSZ H13 IL9 IPNFZ M4Z RIG TDBHL TFL UNPAY |
| ID | FETCH-LOGICAL-c429t-c22e3f62e3a63b3dc5dddfad07360da2f45802a783bb2cdfce3daa8a4eb94f0b3 |
| IEDL.DBID | DOA |
| ISSN | 1875-6883 1875-6891 |
| IngestDate | Fri Oct 03 12:50:45 EDT 2025 Wed Oct 01 15:21:29 EDT 2025 Tue Oct 21 12:48:25 EDT 2025 Thu Apr 24 22:53:38 EDT 2025 Tue Jul 01 01:20:21 EDT 2025 Fri Feb 21 02:44:40 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Conditional image synthesis Patch strategy Fusion strategy Melanoma |
| Language | English |
| License | cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c429t-c22e3f62e3a63b3dc5dddfad07360da2f45802a783bb2cdfce3daa8a4eb94f0b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-9960-0839 |
| OpenAccessLink | https://doaj.org/article/d0a95e16027e4d8aa1894c023bcb8f39 |
| PQID | 3072744014 |
| PQPubID | 4869256 |
| PageCount | 18 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_d0a95e16027e4d8aa1894c023bcb8f39 unpaywall_primary_10_1007_s44196_023_00246_1 proquest_journals_3072744014 crossref_primary_10_1007_s44196_023_00246_1 crossref_citationtrail_10_1007_s44196_023_00246_1 springer_journals_10_1007_s44196_023_00246_1 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2023-05-19 |
| PublicationDateYYYYMMDD | 2023-05-19 |
| PublicationDate_xml | – month: 05 year: 2023 text: 2023-05-19 day: 19 |
| PublicationDecade | 2020 |
| PublicationPlace | Dordrecht |
| PublicationPlace_xml | – name: Dordrecht – name: Abingdon |
| PublicationTitle | International journal of computational intelligence systems |
| PublicationTitleAbbrev | Int J Comput Intell Syst |
| PublicationYear | 2023 |
| Publisher | Springer Netherlands Springer Nature B.V Springer |
| Publisher_xml | – name: Springer Netherlands – name: Springer Nature B.V – name: Springer |
| References | WuJSkin lesion classification using densely connected convolutional networks with attention residual learningSensors20202024708010.3390/s20247080 DingSDeep attention branch networks for skin lesion classificationComput. Methods Programs Biomed.202121210.1016/j.cmpb.2021.106447 Zhu, J.Y., Park, T., Isola, P., & Efros, A. A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. IEEE. (2017) KazeminiaSBaurCKuijperAGANs for medical image analysis[J]Artif. Intell. Med.202010910.1016/j.artmed.2020.101938 Gutman, D. et al.: Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). (2016). Available: https://arxiv.org/abs/1605.01397 Isola, P., et al.: Image-to-image translation with conditional adversarial networks. IEEE Conf. Comput. Vis. Pattern Recogn. 22339–22366 (2016) Devries, T., & Ramachandram, D.: Skin lesion classification using deep multi-scale convolutional neural networks (2017) XiaoYActive jamming recognition based on bilinear EfficientNet and attention mechanismIET Radar Sonar Navigat202115995796810.1049/rsn2.12089 Bi, L., Kim, J., Ahn, E., & Feng, D.: Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks (2017) Tan, M., and Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. (2019) TangPGP-CNN-DTEL: global-part CNN model with data-transformed ensemble learning for skin lesion classificationIEEE J. Biomed. Health Inform.202010.1109/JBHI.2020.2977013 Jetley, S. , Lord, N. A. , Lee, N. , & Torr, P. H. S. . (2018). Learn To Pay Attention. ZhangJXieYXiaYShenCAttention residual learning for skin lesion classificationIEEE Trans. Med. Imaging.201910.1109/TMI.2019.2893944 AbbasKAbbasiADongSApplication of network link prediction in drug discovery[J]BMC Bioinform.202122112110.1186/s12859-021-04082-y Ha, Q., Liu, B., and Liu, F.: Identifying melanoma images using EfficientNet ensemble: winning solution to the siim-isic melanoma classification challenge. (2020) Perez, F., et al.: Data augmentation for skin lesion analysis. ISIC Skin Image Analysis Workshop and Challenge @ MICCAI 2018 (2018) ZhangJXieYWuQXiaYMedical image classification using synergic deep learningMed. Image Anal.201910.1016/j.media.2019.02.010 Wolterink J.M., Dinkla A.M., Savenije M, et al.: Deep MR to CT Synthesis using Unpaired Data[J]. arXiv e-prints, 2017 Codella, N., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., & Dusza, S.W., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic) (2017) Díaz, I.G.: Incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions. IEEE J. Biomed. Health Inform. 547–559 (2017) YanYKawaharaJHamarnehGMelanoma Recognition via Visual Attention2019ChamSpringer10.1007/978-3-030-20351-1_62 Goodfellow, I.: NIPS 2016 tutorial: generative adversarial networks. arXiv preprint arXiv:1701.00160, 2016. 2, 4, 5 Menegola, A., Tavares, J., Fornaciali, M., Lin, T.L., & Valle, E.: Recod titans at isic challenge 2017 (2017) Matsunaga, K., Hamada, A., Minagawa, A., & Koga, H.: Image classification of melanoma, nevus and seborrheic keratosis by deep neural network ensemble (2017) KawaharaJSeven-point checklist and skin lesion classification using multitask multimodal neural netsIEEE J. Biomed. Health Inform.20182353854610.1109/JBHI.2018.2824327 Zhu, J.Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. IEEE 800–815 (2017) HyvarinenAHoyerPMelBEmergence of phase-and shift-invariant features by decomposition of natural images into independent feature subspacesNeural Comput.20001271705172010.1162/089976600300015312 Zunair, H., Hamza, A.B.: Melanoma detection using adversarial training and deep transfer learning[J]. 2020(13). BalazsHSkin lesion classification with ensembles of deep convolutional neural networksJ. Biomed. Inform.201886S1532046418301618 AlmarazdamianJAPonomaryovVSadovnychiySCastillejosfernandezHMelanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measuresEntropy202022448410.3390/e22040484 Yang, X., et al.: Skin lesion analysis by multi-target deep neural networks. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) IEEE, 2018. WeeseJLorenzCFour challenges in medical image analysis from an industrial perspectiveMed. Image Anal.201633444910.1016/j.media.2016.06.023 KimDHongBWUnsupervised feature elimination via generative adversarial networks: application to hair removal in melanoma classificationIEEE Access202110.1109/ACCESS.2021.3065701 NidaNIrtazaAHaroon YousafMA novel region-extreme convolutional neural network for melanoma malignancy recognitionMath. Prob. Eng.202110.1155/2021/6671498Article ID 6671498 YuLAutomated melanoma recognition in dermoscopy images via very deep residual networksIEEE Trans. Med. Imaging2016999941004 Gessert, N. , Sentker, T. , Madesta, F. , Schmitz, R. , & Schlaefer, A. . (2019). Skin lesion classification using cnns with patch-based attention and diagnosis-guided loss weighting. IEEE Trans. Biomed. Eng. PP(99), 1–1. CodellaNCDeep learning ensembles for melanoma recognition in dermoscopy imagesIBM J. Res. Dev.2017614115 WolterinkJMDinklaAMSavenijeMSeevinckPRIgumIDeep MR to CT synthesis using unpaired data. International workshop on simulation and synthesis in medical imaging2017ChamSpringer BinderMSchwarzMWinklerASteinerAPehambergerHEpiluminescence microscopy. a useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologistsArch. Dermatol.1995131328629110.1001/archderm.1995.01690150050011 GansterHPinzAAutomated melanoma recognitionIEEE Trans. Med. Imaging200110.1109/42.918473 RonnebergerOFischerPBroxTU-Net: Convolutional Networks for Biomedical Image Segmentation2015Springer International Publishing DongSWangPAbbasKA survey on deep learning and its applications[J]Comput. Sci. Rev.202140423335410.1016/j.cosrev.2021.1003791484.68205 Balch, C., Gershenwald, M., Jeffrey, E., Soong, S.-j., et al.: Final Version of 2009 AJCC melanoma staging and classification ZunairHHamzaABMelanoma detection using adversarial training and deep transfer learningPhys. Med. Biol.202010.1088/1361-6560/ab86d3 de Bel, T., Hermsen, M., Kers, J., van der Laak, J., and Litjens, G.: Stain-transforming cycle-consistent generative adversarial networks for improved segmentation of renal histopathology. In Proc. International Conference on Medical Imaging with Deep Learning, vol. 102, pp. 151–163, (2018) ZhaoZSunQYangHQiaoHWuDOCompression artifacts reduction by improved generative adversarial networksEURASIP J. Image Video Process.201910.1186/s13640-019-0465-0 Yx, A., Jw, B., and Zl, C.: Cancer diagnosis using generative adversarial networks based on deep learning from imbalanced data. Comput. Biol. Med. 104540–104540 (2021) Song, J., Li, J., Ma, S., Tang, J., & Guo, F.: Melanoma Classification in Dermoscopy Images via Ensemble Learning on Deep Neural Network. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE. (2020) TangPEfficient skin lesion segmentation using separable-Unet with stochastic weight averagingComput. Methods Programs Biomed.201917828930110.1016/j.cmpb.2019.07.005 MahbodASchaeferGEllingerIEckerRPitiotAWangCFusing fine-tuned deep features for skin lesion classificationComput. Med. Imaging Graph.201971192910.1016/j.compmedimag.2018.10.007 SonHMAI-based localization and classification of skin disease with erythemaSci. Rep.20211115350535010.1038/s41598-021-84593-z Ko, et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 115–118 (2017) LiuZZerubiaJSkin image illumination modeling and chromophore identication for melanoma diagnosisPhys. Med. Biol.20156093415343110.1088/0031-9155/60/9/3415 246_CR37 HM Son (246_CR26) 2021; 11 246_CR38 246_CR35 246_CR34 J Weese (246_CR8) 2016; 33 J Zhang (246_CR5) 2019 Z Zhao (246_CR24) 2019 H Zunair (246_CR20) 2020 246_CR28 L Yu (246_CR49) 2016; 99 246_CR25 K Abbas (246_CR45) 2021; 22 A Hyvarinen (246_CR4) 2000; 12 246_CR23 P Tang (246_CR7) 2020 246_CR21 S Dong (246_CR46) 2021; 40 NC Codella (246_CR51) 2017; 61 246_CR19 246_CR17 246_CR18 246_CR15 D Kim (246_CR33) 2021 N Nida (246_CR52) 2021 246_CR16 246_CR13 H Balazs (246_CR12) 2018; 86 246_CR6 246_CR10 P Tang (246_CR29) 2019; 178 246_CR50 M Binder (246_CR2) 1995; 131 246_CR1 Z Liu (246_CR31) 2015; 60 J Wu (246_CR36) 2020; 20 Y Yan (246_CR9) 2019 JM Wolterink (246_CR22) 2017 246_CR48 246_CR47 246_CR44 J Zhang (246_CR11) 2019 246_CR42 246_CR40 S Kazeminia (246_CR41) 2020; 109 H Ganster (246_CR3) 2001 JA Almarazdamian (246_CR30) 2020; 22 A Mahbod (246_CR14) 2019; 71 O Ronneberger (246_CR43) 2015 Y Xiao (246_CR27) 2021; 15 S Ding (246_CR53) 2021; 212 246_CR39 J Kawahara (246_CR32) 2018; 23 |
| References_xml | – reference: KawaharaJSeven-point checklist and skin lesion classification using multitask multimodal neural netsIEEE J. Biomed. Health Inform.20182353854610.1109/JBHI.2018.2824327 – reference: DongSWangPAbbasKA survey on deep learning and its applications[J]Comput. Sci. Rev.202140423335410.1016/j.cosrev.2021.1003791484.68205 – reference: HyvarinenAHoyerPMelBEmergence of phase-and shift-invariant features by decomposition of natural images into independent feature subspacesNeural Comput.20001271705172010.1162/089976600300015312 – reference: Isola, P., et al.: Image-to-image translation with conditional adversarial networks. IEEE Conf. Comput. Vis. Pattern Recogn. 22339–22366 (2016) – reference: WuJSkin lesion classification using densely connected convolutional networks with attention residual learningSensors20202024708010.3390/s20247080 – reference: Zunair, H., Hamza, A.B.: Melanoma detection using adversarial training and deep transfer learning[J]. 2020(13). – reference: Zhu, J.Y., Park, T., Isola, P., & Efros, A. A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. IEEE. (2017) – reference: BinderMSchwarzMWinklerASteinerAPehambergerHEpiluminescence microscopy. a useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologistsArch. Dermatol.1995131328629110.1001/archderm.1995.01690150050011 – reference: Menegola, A., Tavares, J., Fornaciali, M., Lin, T.L., & Valle, E.: Recod titans at isic challenge 2017 (2017) – reference: CodellaNCDeep learning ensembles for melanoma recognition in dermoscopy imagesIBM J. Res. Dev.2017614115 – reference: Gessert, N. , Sentker, T. , Madesta, F. , Schmitz, R. , & Schlaefer, A. . (2019). Skin lesion classification using cnns with patch-based attention and diagnosis-guided loss weighting. IEEE Trans. Biomed. Eng. PP(99), 1–1. – reference: Matsunaga, K., Hamada, A., Minagawa, A., & Koga, H.: Image classification of melanoma, nevus and seborrheic keratosis by deep neural network ensemble (2017) – reference: GansterHPinzAAutomated melanoma recognitionIEEE Trans. Med. Imaging200110.1109/42.918473 – reference: Díaz, I.G.: Incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions. IEEE J. Biomed. Health Inform. 547–559 (2017) – reference: ZhangJXieYWuQXiaYMedical image classification using synergic deep learningMed. Image Anal.201910.1016/j.media.2019.02.010 – reference: Goodfellow, I.: NIPS 2016 tutorial: generative adversarial networks. arXiv preprint arXiv:1701.00160, 2016. 2, 4, 5 – reference: Devries, T., & Ramachandram, D.: Skin lesion classification using deep multi-scale convolutional neural networks (2017) – reference: ZhangJXieYXiaYShenCAttention residual learning for skin lesion classificationIEEE Trans. Med. Imaging.201910.1109/TMI.2019.2893944 – reference: NidaNIrtazaAHaroon YousafMA novel region-extreme convolutional neural network for melanoma malignancy recognitionMath. Prob. Eng.202110.1155/2021/6671498Article ID 6671498 – reference: SonHMAI-based localization and classification of skin disease with erythemaSci. Rep.20211115350535010.1038/s41598-021-84593-z – reference: ZhaoZSunQYangHQiaoHWuDOCompression artifacts reduction by improved generative adversarial networksEURASIP J. Image Video Process.201910.1186/s13640-019-0465-0 – reference: Perez, F., et al.: Data augmentation for skin lesion analysis. ISIC Skin Image Analysis Workshop and Challenge @ MICCAI 2018 (2018) – reference: KimDHongBWUnsupervised feature elimination via generative adversarial networks: application to hair removal in melanoma classificationIEEE Access202110.1109/ACCESS.2021.3065701 – reference: Balch, C., Gershenwald, M., Jeffrey, E., Soong, S.-j., et al.: Final Version of 2009 AJCC melanoma staging and classification – reference: XiaoYActive jamming recognition based on bilinear EfficientNet and attention mechanismIET Radar Sonar Navigat202115995796810.1049/rsn2.12089 – reference: Song, J., Li, J., Ma, S., Tang, J., & Guo, F.: Melanoma Classification in Dermoscopy Images via Ensemble Learning on Deep Neural Network. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE. (2020) – reference: Wolterink J.M., Dinkla A.M., Savenije M, et al.: Deep MR to CT Synthesis using Unpaired Data[J]. arXiv e-prints, 2017 – reference: KazeminiaSBaurCKuijperAGANs for medical image analysis[J]Artif. Intell. Med.202010910.1016/j.artmed.2020.101938 – reference: Ha, Q., Liu, B., and Liu, F.: Identifying melanoma images using EfficientNet ensemble: winning solution to the siim-isic melanoma classification challenge. (2020) – reference: AlmarazdamianJAPonomaryovVSadovnychiySCastillejosfernandezHMelanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measuresEntropy202022448410.3390/e22040484 – reference: Gutman, D. et al.: Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). (2016). Available: https://arxiv.org/abs/1605.01397 – reference: YanYKawaharaJHamarnehGMelanoma Recognition via Visual Attention2019ChamSpringer10.1007/978-3-030-20351-1_62 – reference: Bi, L., Kim, J., Ahn, E., & Feng, D.: Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks (2017) – reference: ZunairHHamzaABMelanoma detection using adversarial training and deep transfer learningPhys. Med. Biol.202010.1088/1361-6560/ab86d3 – reference: de Bel, T., Hermsen, M., Kers, J., van der Laak, J., and Litjens, G.: Stain-transforming cycle-consistent generative adversarial networks for improved segmentation of renal histopathology. In Proc. International Conference on Medical Imaging with Deep Learning, vol. 102, pp. 151–163, (2018) – reference: MahbodASchaeferGEllingerIEckerRPitiotAWangCFusing fine-tuned deep features for skin lesion classificationComput. Med. Imaging Graph.201971192910.1016/j.compmedimag.2018.10.007 – reference: Yx, A., Jw, B., and Zl, C.: Cancer diagnosis using generative adversarial networks based on deep learning from imbalanced data. Comput. Biol. Med. 104540–104540 (2021) – reference: BalazsHSkin lesion classification with ensembles of deep convolutional neural networksJ. Biomed. Inform.201886S1532046418301618 – reference: Ko, et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 115–118 (2017) – reference: RonnebergerOFischerPBroxTU-Net: Convolutional Networks for Biomedical Image Segmentation2015Springer International Publishing – reference: Codella, N., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., & Dusza, S.W., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic) (2017) – reference: Jetley, S. , Lord, N. A. , Lee, N. , & Torr, P. H. S. . (2018). Learn To Pay Attention. – reference: YuLAutomated melanoma recognition in dermoscopy images via very deep residual networksIEEE Trans. Med. Imaging2016999941004 – reference: TangPEfficient skin lesion segmentation using separable-Unet with stochastic weight averagingComput. Methods Programs Biomed.201917828930110.1016/j.cmpb.2019.07.005 – reference: WeeseJLorenzCFour challenges in medical image analysis from an industrial perspectiveMed. Image Anal.201633444910.1016/j.media.2016.06.023 – reference: TangPGP-CNN-DTEL: global-part CNN model with data-transformed ensemble learning for skin lesion classificationIEEE J. Biomed. Health Inform.202010.1109/JBHI.2020.2977013 – reference: WolterinkJMDinklaAMSavenijeMSeevinckPRIgumIDeep MR to CT synthesis using unpaired data. International workshop on simulation and synthesis in medical imaging2017ChamSpringer – reference: LiuZZerubiaJSkin image illumination modeling and chromophore identication for melanoma diagnosisPhys. Med. Biol.20156093415343110.1088/0031-9155/60/9/3415 – reference: DingSDeep attention branch networks for skin lesion classificationComput. Methods Programs Biomed.202121210.1016/j.cmpb.2021.106447 – reference: AbbasKAbbasiADongSApplication of network link prediction in drug discovery[J]BMC Bioinform.202122112110.1186/s12859-021-04082-y – reference: Yang, X., et al.: Skin lesion analysis by multi-target deep neural networks. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) IEEE, 2018. – reference: Zhu, J.Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. IEEE 800–815 (2017) – reference: Tan, M., and Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. (2019) – volume: 99 start-page: 994 year: 2016 ident: 246_CR49 publication-title: IEEE Trans. Med. Imaging – volume: 60 start-page: 3415 issue: 9 year: 2015 ident: 246_CR31 publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/60/9/3415 – volume: 40 year: 2021 ident: 246_CR46 publication-title: Comput. Sci. Rev. doi: 10.1016/j.cosrev.2021.100379 – ident: 246_CR48 – year: 2019 ident: 246_CR5 publication-title: IEEE Trans. Med. Imaging. doi: 10.1109/TMI.2019.2893944 – ident: 246_CR13 doi: 10.1109/ISBI.2018.8363547 – ident: 246_CR21 – volume: 71 start-page: 19 year: 2019 ident: 246_CR14 publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2018.10.007 – ident: 246_CR16 – ident: 246_CR50 doi: 10.1007/978-3-030-01201-4_33 – ident: 246_CR25 doi: 10.1109/ICCV.2017.244 – volume: 178 start-page: 289 year: 2019 ident: 246_CR29 publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2019.07.005 – volume: 61 start-page: 1 issue: 4 year: 2017 ident: 246_CR51 publication-title: IBM J. Res. Dev. – volume: 12 start-page: 1705 issue: 7 year: 2000 ident: 246_CR4 publication-title: Neural Comput. doi: 10.1162/089976600300015312 – ident: 246_CR47 – year: 2020 ident: 246_CR7 publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2020.2977013 – ident: 246_CR15 – ident: 246_CR39 doi: 10.1109/ICCV.2017.244 – volume: 11 start-page: 5350 issue: 1 year: 2021 ident: 246_CR26 publication-title: Sci. Rep. doi: 10.1038/s41598-021-84593-z – year: 2020 ident: 246_CR20 publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/ab86d3 – volume: 20 start-page: 7080 issue: 24 year: 2020 ident: 246_CR36 publication-title: Sensors doi: 10.3390/s20247080 – ident: 246_CR34 doi: 10.1109/BIBM49941.2020.9313451 – ident: 246_CR40 doi: 10.1088/1361-6560/ab86d3 – volume: 22 start-page: 484 issue: 4 year: 2020 ident: 246_CR30 publication-title: Entropy doi: 10.3390/e22040484 – volume: 22 start-page: 1 issue: 1 year: 2021 ident: 246_CR45 publication-title: BMC Bioinform. doi: 10.1186/s12859-021-04082-y – volume: 86 start-page: S15320464183016 year: 2018 ident: 246_CR12 publication-title: J. Biomed. Inform. – ident: 246_CR10 – volume-title: Deep MR to CT synthesis using unpaired data. International workshop on simulation and synthesis in medical imaging year: 2017 ident: 246_CR22 – volume: 109 year: 2020 ident: 246_CR41 publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2020.101938 – volume: 131 start-page: 286 issue: 3 year: 1995 ident: 246_CR2 publication-title: Arch. Dermatol. doi: 10.1001/archderm.1995.01690150050011 – volume-title: U-Net: Convolutional Networks for Biomedical Image Segmentation year: 2015 ident: 246_CR43 – ident: 246_CR38 doi: 10.1007/978-3-319-68127-6_2 – year: 2019 ident: 246_CR24 publication-title: EURASIP J. Image Video Process. doi: 10.1186/s13640-019-0465-0 – year: 2021 ident: 246_CR52 publication-title: Math. Prob. Eng. doi: 10.1155/2021/6671498 – ident: 246_CR23 – ident: 246_CR18 – ident: 246_CR42 – volume-title: Melanoma Recognition via Visual Attention year: 2019 ident: 246_CR9 doi: 10.1007/978-3-030-20351-1_62 – year: 2001 ident: 246_CR3 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.918473 – ident: 246_CR6 – volume: 15 start-page: 957 issue: 9 year: 2021 ident: 246_CR27 publication-title: IET Radar Sonar Navigat doi: 10.1049/rsn2.12089 – ident: 246_CR35 doi: 10.1109/EMBC.2018.8512488 – volume: 33 start-page: 44 year: 2016 ident: 246_CR8 publication-title: Med. Image Anal. doi: 10.1016/j.media.2016.06.023 – ident: 246_CR28 – year: 2019 ident: 246_CR11 publication-title: Med. Image Anal. doi: 10.1016/j.media.2019.02.010 – volume: 212 year: 2021 ident: 246_CR53 publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2021.106447 – ident: 246_CR19 doi: 10.1016/j.compbiomed.2021.104540 – year: 2021 ident: 246_CR33 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3065701 – ident: 246_CR17 – volume: 23 start-page: 538 year: 2018 ident: 246_CR32 publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2018.2824327 – ident: 246_CR1 – ident: 246_CR37 doi: 10.1038/nature21056 – ident: 246_CR44 doi: 10.1109/CVPR.2017.632 |
| SSID | ssj0002140044 ssib050732782 |
| Score | 2.3416953 |
| Snippet | Melanoma is a fatal skin disease, and there are many challenging tasks in the detection of melanoma through neural network at this stage. We propose a new... Abstract Melanoma is a fatal skin disease, and there are many challenging tasks in the detection of melanoma through neural network at this stage. We propose a... |
| SourceID | doaj unpaywall proquest crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Algorithms Artificial Intelligence Artificial neural networks Classification Computational Intelligence Conditional image synthesis Control Diagnosis Engineering Fusion strategy Lesions Mathematical Logic and Foundations Mechatronics Medical imaging Melanoma Neural networks Patch strategy Research Article Robotics Skin injuries Synthesis |
| SummonAdditionalLinks | – databaseName: SpringerOpen dbid: C24 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6hcoAeeBRQFwrygRu1FD_yOral1QqpFQcq9WbZHhuQVknVzQr13zPxOqGVUAWXKIofSmY8r9jzDcDHsmlQihB47arANWLNXVSaR0XhCBbO1QmO4fyiWl7qL1flVU4KW0-n3actyaSp52Q3MtzpwKzio2GpOMU8j8n_kGPBhpOc4zDqXynGdalzhszfh96zQgms_56HOW-K7sKTTXdtb3_Z1eqO3Tl7Ac-yw8iOthx-CY9CtwfPp2IMLMvmHuzeQRZ8BcujzdAnNFb2eXuY7uea9ZGdh5XtqIEdk_VC1nfsNGFIkOm5CAOzHbKvpJx_sIxae_saLs9Ov50seS6awD2ZloF7KYOKFV1spZxCXyJitEiiXBVoZdRlU0hbN8o56TH6oNDaxurgWh0Lp97ATtd3YR9YK1wpbIGu9hSluMKJWOggfT0-x1AuQExEND4jio-FLVZmxkJOhDdEeJMIb8QCPs1jrrd4Gg_2Ph55M_ccsbDTg_7mu8miZbCwbRlERQF20NhYK5pWe5rFeddE1S7gYOKsyQK6NqTaEjai0As4nLj9p_mhVzqcV8Q_fMHb_5v9HTyVdLf9x3MAO8PNJrwnr2dwH9Ii_w1ZLPa1 priority: 102 providerName: Springer Nature – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB6V7QE4UJ5ioSAfuFG3cewkznFLW62QuuqBlcopsj02IFbJqs2qan89juMEilAF4hJFthP5MZ5vRp75DPAukxJTZi0tdG6pQCyodlxQx707gonWRaBjOF3k86X4eJ6db8HRkAsTot2HI8k-p6FjaarbgzW6gzHxzYN4CJ7ltAOZnHp9g-4ebOeZt8gnsL1cnM0-d76WN8dpLgMbZ3wvWcyd-fOPbuFToPG_ZXuOx6UP4f6mXqvrK7Va_YJIJztgh7H0gSjf9zet3jc3v9E8_u9gH8OjaLKSWS9jT2DL1k9hZ7gOgkTt8Azms03bBAZYctQH8H27JI0jp3alal9BDj1iImlqchx4K3wPFrYlqkZy5gHhK4lMudfPYXly_OnDnMaLGqjxcNZSk6aWu9w_VM41R5MholPo1UeeoEqdyGSSqkJyrVODzliOSkklrC6FSzR_AZO6qe1LICXTGVMJ6sJ4z0gnmrlE2NQUXTnabApsWJ7KRBbz7jKNVTXyL4fpqvx0VWG6KjaF9-M3657D487Wh92qjy07_u1Q0Fx8qeJ2rjBRZWZZ7p16K1AqxWQpjP-LNlo6Xk5hd5CZKiqFy8qr08DHyMQU9oZl_1l9V5f2Rln7ixG8-rfmr-FBGoQro6zchUl7sbFvvKXV6rdxI_0A8-gcWg priority: 102 providerName: Unpaywall |
| Title | Automatic Diagnosis of Melanoma Based on EfficientNet and Patch Strategy |
| URI | https://link.springer.com/article/10.1007/s44196-023-00246-1 https://www.proquest.com/docview/3072744014 https://link.springer.com/content/pdf/10.1007/s44196-023-00246-1.pdf https://doaj.org/article/d0a95e16027e4d8aa1894c023bcb8f39 |
| UnpaywallVersion | publishedVersion |
| Volume | 16 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Open Access Full Text customDbUrl: eissn: 1875-6883 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002140044 issn: 1875-6883 databaseCode: DOA dateStart: 20080101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1875-6883 dateEnd: 99991231 omitProxy: true ssIdentifier: ssib050732782 issn: 1875-6891 databaseCode: M~E dateStart: 20120101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAVX databaseName: Springer Nature HAS Fully OA customDbUrl: eissn: 1875-6883 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002140044 issn: 1875-6883 databaseCode: AAJSJ dateStart: 20080101 isFulltext: true titleUrlDefault: https://www.springernature.com providerName: Springer Nature – providerCode: PRVAVX databaseName: Springer Nature OA Free Journals customDbUrl: eissn: 1875-6883 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002140044 issn: 1875-6883 databaseCode: C6C dateStart: 20211201 isFulltext: true titleUrlDefault: http://www.springeropen.com/ providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerOpen customDbUrl: eissn: 1875-6883 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002140044 issn: 1875-6883 databaseCode: C24 dateStart: 20211201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB1BOQAHvhELZeUDN2rhryTOcbu0WiF11QMrlVNke2yBtMpWNKuq_762kw3bS-HAxQfbkezxs59H8bwB-FRojYJ7TytbeqoQK2qDVDTI6I4gs7bKcgxny3KxUt8uiou9VF_pTVgvD9wb7gsyUxeel9F98gq1MVzXykWmsc7qIHPoHtP1njOVzmDBEzbVECWTY-Ui7-f3tpImXiopv8NEWbD_zi1z_DH6FB5v20tzc23W6z3uOX0Bz4ZLI5n1g30JD3z7Cp7vEjKQYX--hsVs222yBiv52j-h-3VFNoGc-bVpYwM5jpyFZNOSk6wcEQln6TtiWiTn8Uj-SQat2ps3sDo9-T5f0CFVAnWRUDrqhPAylLEwpbQSXYGIwWDcwCVDI4IqNBOm0tJa4TA4L9EYbZS3tQrMyrdw0G5a_w5IzW3BDUNbueibWGZ5YMoLV6V69MUE-M5sjRt0xFM6i3UzKiBnUzfR1E02dcMn8Hn85rJX0bi393FajbFnUsDOFREXzYCL5m-4mMDhbi2bYVteNfFAy4qIXE3gaLe-f5rvG9LRiIF_mMH7_zGDD_BEZLgWlNeHcND93vqP8QbU2Sk8nAuVynI-zcCfwqPV8nz24xaK-QGO |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Jb9QwFH5C7aH0wFJADBTwgRu1FC_ZjlNoNQydERKt1Jtl-9mANEqqTkao_x7H44RWQhVccvCm5C3-_OLnzwDv86pCzpyjpSkclYglNV5I6kUIRzAzpox0DItlMbuQ88v8Mh0KWw_Z7sOWZJypx8NuAbhjwqygPbAUNMQ8u32SVXDH3el0_m0-_lvhrLdMmc7I_L3zHRyKdP131pjjtug-7G2aK33zS69Wt5Dn9Ak8SktGMt3q-Ck8cM0BPB6uYyDJOw9g_xa34DOYTTddG_lYyadtOt3PNWk9WbiVbkIFOQ74haRtyElkkQjgs3Qd0Q2Sr2F6_kESb-3Nc7g4PTn_OKPp2gRqA7h01HLuhC_CQxfCCLQ5InqNwZmLDDX3Mq8yrstKGMMteusEal1p6UwtfWbEC9hp2sa9BFIzkzOdoSltiFNMZpjPpOO27MvR5RNggxCVTZzi_dUWKzWyIUfBqyB4FQWv2AQ-jH2utowa97Y-7nUztuzZsGNBe_1dJedSmOk6d6wIIbaTWGnNqlraMIqxpvKinsDhoFmVXHStwuQW2RGZnMDRoO0_1fe90tFoEf_wBa_-b_R3sDc7X5yps8_LL6_hIY8mm1NWH8JOd71xb8IaqDNvk8n_Bo6Z-zU |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwEB5VINFyAEpbdVtofeitWMSPvI6wsNo-WHEoEjfL9thtpVWygqwq_n0db5IuEkL0koNfSubh8cQz3wB8SosCOXOO5iZzVCLm1HghqRfBHcHEmDzCMVzMsumV_HqdXq9l8cdo9_5KcpXT0KI0Vc3xAv3xkPgWjHgMnhW0NTIZDf7PpgzWra1hMM7Gw18WzloZlV22zMNT71mkCNx_77Q5XJBuw_NltdB3f_R8vmaDJnuw0x0eycmK2y_hmav2YbcvzEA6Pd2H7TWUwVcwPVk2dURmJWerwLrft6T25MLNdRU6yGmwZEjqipxHPIlAhplriK6QXIaN-hfpEGzvXsPV5PzHeEq7AgrUBjPTUMu5Ez4LD50JI9CmiOg1BrXOEtTcy7RIuM4LYQy36K0TqHWhpTOl9IkRb2Cjqiv3FkjJTMp0gia3wWMxiWE-kY7bvG1Hl46A9URUtkMXb4tczNWAixwJrwLhVSS8YiP4PMxZrLA1Hh192vJmGNniYseG-uan6tRMYaLL1LEsONtOYqE1K0ppwyrGmsKLcgQHPWdVp6y3KmxzESeRyREc9dz-1_3YKx0NEvGEL3j3f6t_hK3Ls4n6_mX27T284FFiU8rKA9hobpbuMByGGvMhyvtfvq_-Eg |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB6V7QE4UJ5ioSAfuFG3cewkznFLW62QuuqBlcopsj02IFbJqs2qan89juMEilAF4hJFthP5MZ5vRp75DPAukxJTZi0tdG6pQCyodlxQx707gonWRaBjOF3k86X4eJ6db8HRkAsTot2HI8k-p6FjaarbgzW6gzHxzYN4CJ7ltAOZnHp9g-4ebOeZt8gnsL1cnM0-d76WN8dpLgMbZ3wvWcyd-fOPbuFToPG_ZXuOx6UP4f6mXqvrK7Va_YJIJztgh7H0gSjf9zet3jc3v9E8_u9gH8OjaLKSWS9jT2DL1k9hZ7gOgkTt8Azms03bBAZYctQH8H27JI0jp3alal9BDj1iImlqchx4K3wPFrYlqkZy5gHhK4lMudfPYXly_OnDnMaLGqjxcNZSk6aWu9w_VM41R5MholPo1UeeoEqdyGSSqkJyrVODzliOSkklrC6FSzR_AZO6qe1LICXTGVMJ6sJ4z0gnmrlE2NQUXTnabApsWJ7KRBbz7jKNVTXyL4fpqvx0VWG6KjaF9-M3657D487Wh92qjy07_u1Q0Fx8qeJ2rjBRZWZZ7p16K1AqxWQpjP-LNlo6Xk5hd5CZKiqFy8qr08DHyMQU9oZl_1l9V5f2Rln7ixG8-rfmr-FBGoQro6zchUl7sbFvvKXV6rdxI_0A8-gcWg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Automatic+Diagnosis+of+Melanoma+Based+on+EfficientNet+and+Patch+Strategy&rft.jtitle=International+journal+of+computational+intelligence+systems&rft.au=Zou+Qingxu&rft.au=Cheng%2C+Jinyong&rft.au=Liang+Zhenlu&rft.date=2023-05-19&rft.pub=Springer+Nature+B.V&rft.issn=1875-6891&rft.eissn=1875-6883&rft.volume=16&rft.issue=1&rft_id=info:doi/10.1007%2Fs44196-023-00246-1&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1875-6883&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1875-6883&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1875-6883&client=summon |