Method for the recovery of images in databases of skin cancer
Deep learning is widely used for the classification of images since the ImageNet competition in 2012 (Zaharia et al. in Common ACM 59(11):56–65, 2016, [1]; Tajbakhsh et al. in IEEE Trans Med Imaging 35(5):1299–1312, 2016, [2]). This image classification is very useful in the field of medicine, in wh...
- Autores:
-
Viloria, Amelec
Varela, Noel
Nuñez-Bravo, Narledys
Pineda Lezama, Omar Bonerge
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7708
- Acceso en línea:
- https://hdl.handle.net/11323/7708
https://doi.org/10.1007/978-981-15-7234-0_94
https://repositorio.cuc.edu.co/
- Palabra clave:
- Deep learning
Medical images
Clinical data analysis
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Method for the recovery of images in databases of skin cancer |
| title |
Method for the recovery of images in databases of skin cancer |
| spellingShingle |
Method for the recovery of images in databases of skin cancer Deep learning Medical images Clinical data analysis |
| title_short |
Method for the recovery of images in databases of skin cancer |
| title_full |
Method for the recovery of images in databases of skin cancer |
| title_fullStr |
Method for the recovery of images in databases of skin cancer |
| title_full_unstemmed |
Method for the recovery of images in databases of skin cancer |
| title_sort |
Method for the recovery of images in databases of skin cancer |
| dc.creator.fl_str_mv |
Viloria, Amelec Varela, Noel Nuñez-Bravo, Narledys Pineda Lezama, Omar Bonerge |
| dc.contributor.author.spa.fl_str_mv |
Viloria, Amelec Varela, Noel Nuñez-Bravo, Narledys Pineda Lezama, Omar Bonerge |
| dc.subject.spa.fl_str_mv |
Deep learning Medical images Clinical data analysis |
| topic |
Deep learning Medical images Clinical data analysis |
| description |
Deep learning is widely used for the classification of images since the ImageNet competition in 2012 (Zaharia et al. in Common ACM 59(11):56–65, 2016, [1]; Tajbakhsh et al. in IEEE Trans Med Imaging 35(5):1299–1312, 2016, [2]). This image classification is very useful in the field of medicine, in which there is a growing interest in the use of data mining techniques in recent years. In this paper, a deep learning network was selected and trained for the analysis of a set of skin cancer data, obtaining very satisfactory results, as the model surpassed the classification results of trained dermatologists using a dermatoscope, other automatic learning techniques, and other deep learning techniques. |
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2021 |
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https://doi.org/10.1007/978-981-15-7234-0_94 |
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eng |
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1. Zaharia M, Xin RS, Wendell P, Das T, Armbrust M, Dave A, Meng X, Rosen J, Venkataraman S, Franklin MJ, Ghodsi A, Gonzalez J, Shenker S, Stoica I (2016) Apache spark: a unified engine for big data processing. Common ACM 59(11):56–65 2. Tajbakhsh N, Shin JY, Gurudu SR, Todd Hurst R, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312 3. Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D (2014) The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd annual meeting of the association for computational linguistics: system demonstrations, pp 55–60 4. Hahsler M, Karpienko R (2017) Visualizing association rules in hierarchical groups. J Bus Econ 87:317–335 5. Alves LGA, Ribeiro HV, Rodrigues FA (2018) Crime prediction through urban metrics and statistical learning. Phys A Stat Mech Appl 505:435–443 6. Silverstein C, Brin S, Motwani R, Ullman J (2000) Scalable techniques for mining causal structures. Data Min Knowl Disc 4(2–3):163–192 7. Amelec V, Carmen V (2015) Relationship between variables of performance social and financial of microfinance institutions. Adv Sci Lett 21(6):1931–1934 8. Amelec V, Lezama OBP (2019) Improvements for determining the number of clusters in k-Means for innovation databases in SMEs. Procedia Comput Sci 151:1201–1206 9. Kamatkar SJ, Kamble A, Viloria A, Hernández-Fernandez L, Cali EG (2018) Database performance tuning and query optimization. In: International conference on data mining and big data, Springer, Cham, pp 3–11 10. Erlandsson F, Brodka P, Borg A, Johnson H (2016) Finding influential users in social media using association rule learning. Entropy 18:164 11. Baculo MJC, Marzan CS (2017) Remedios de Dios Bulos, and Conrado Ruiz. Geospatial-temporal analysis and classification of criminal data in manila. In: Proceedings of 2nd IEEE international conference on computational intelligence and applications, IEEE, pp 6–11 12. Amelec V et al (2019) Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput Sci 155:575–580 13. Clougherty E, Clougherty J, Liu X, Brown D (2015) Spatial and temporal analysis of sex crimes in Charlottesville, Virginia. In: Proceedings of IEEE systems and information engineering design symposium, IEEE, pp 69–74 14. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M et al (2016) Tensorflow: a system for large-scale machine learning. OSDI 16:265–283 15. Codella NCF, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H et al (2017) 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). In: 2018 IEEE 15th International symposium on biomedical imaging (ISBI 2018), IEEE, pp 168–172 16. Iavindrasana J, Cohen G, Depeursinge A, Müller H, Meyer R, Geissbuhler A (2009) Clinical data mining: a review. Yearb Med Informatics 18(01):121–133 17. Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871–1874 18. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explorations 11(1):10–18 19. Kang H-W, Kang H-B (2017) Prediction of crime occurrence from multimodal data using deep learning. PLoS ONE 12(4):e0176244 20. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436 21. Leitão JC, Miotto JM, Gerlach M, Altmann EG (2016) Is this scaling nonlinear? Roy Soc Open Sci 3(7):25–36 22. Gutman D, Codella NCF, Celebi E, Helba B, Marchetti M, Mishra N, Halpern A (2016) 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) |
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Viloria, AmelecVarela, NoelNuñez-Bravo, NarledysPineda Lezama, Omar Bonerge2021-01-18T17:39:40Z2021-01-18T17:39:40Z2021https://hdl.handle.net/11323/7708https://doi.org/10.1007/978-981-15-7234-0_94Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Deep learning is widely used for the classification of images since the ImageNet competition in 2012 (Zaharia et al. in Common ACM 59(11):56–65, 2016, [1]; Tajbakhsh et al. in IEEE Trans Med Imaging 35(5):1299–1312, 2016, [2]). This image classification is very useful in the field of medicine, in which there is a growing interest in the use of data mining techniques in recent years. In this paper, a deep learning network was selected and trained for the analysis of a set of skin cancer data, obtaining very satisfactory results, as the model surpassed the classification results of trained dermatologists using a dermatoscope, other automatic learning techniques, and other deep learning techniques.Viloria, AmelecVarela, NoelNuñez-Bravo, NarledysPineda Lezama, Omar Bonergeapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_94Deep learningMedical imagesClinical data analysisMethod for the recovery of images in databases of skin cancerArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Zaharia M, Xin RS, Wendell P, Das T, Armbrust M, Dave A, Meng X, Rosen J, Venkataraman S, Franklin MJ, Ghodsi A, Gonzalez J, Shenker S, Stoica I (2016) Apache spark: a unified engine for big data processing. Common ACM 59(11):56–652. Tajbakhsh N, Shin JY, Gurudu SR, Todd Hurst R, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–13123. Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D (2014) The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd annual meeting of the association for computational linguistics: system demonstrations, pp 55–604. Hahsler M, Karpienko R (2017) Visualizing association rules in hierarchical groups. J Bus Econ 87:317–3355. Alves LGA, Ribeiro HV, Rodrigues FA (2018) Crime prediction through urban metrics and statistical learning. Phys A Stat Mech Appl 505:435–4436. Silverstein C, Brin S, Motwani R, Ullman J (2000) Scalable techniques for mining causal structures. Data Min Knowl Disc 4(2–3):163–1927. Amelec V, Carmen V (2015) Relationship between variables of performance social and financial of microfinance institutions. Adv Sci Lett 21(6):1931–19348. Amelec V, Lezama OBP (2019) Improvements for determining the number of clusters in k-Means for innovation databases in SMEs. Procedia Comput Sci 151:1201–12069. Kamatkar SJ, Kamble A, Viloria A, Hernández-Fernandez L, Cali EG (2018) Database performance tuning and query optimization. In: International conference on data mining and big data, Springer, Cham, pp 3–1110. Erlandsson F, Brodka P, Borg A, Johnson H (2016) Finding influential users in social media using association rule learning. Entropy 18:16411. Baculo MJC, Marzan CS (2017) Remedios de Dios Bulos, and Conrado Ruiz. Geospatial-temporal analysis and classification of criminal data in manila. In: Proceedings of 2nd IEEE international conference on computational intelligence and applications, IEEE, pp 6–1112. Amelec V et al (2019) Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput Sci 155:575–58013. Clougherty E, Clougherty J, Liu X, Brown D (2015) Spatial and temporal analysis of sex crimes in Charlottesville, Virginia. In: Proceedings of IEEE systems and information engineering design symposium, IEEE, pp 69–7414. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M et al (2016) Tensorflow: a system for large-scale machine learning. OSDI 16:265–28315. Codella NCF, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H et al (2017) 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). In: 2018 IEEE 15th International symposium on biomedical imaging (ISBI 2018), IEEE, pp 168–17216. Iavindrasana J, Cohen G, Depeursinge A, Müller H, Meyer R, Geissbuhler A (2009) Clinical data mining: a review. Yearb Med Informatics 18(01):121–13317. Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871–187418. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explorations 11(1):10–1819. Kang H-W, Kang H-B (2017) Prediction of crime occurrence from multimodal data using deep learning. PLoS ONE 12(4):e017624420. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):43621. Leitão JC, Miotto JM, Gerlach M, Altmann EG (2016) Is this scaling nonlinear? Roy Soc Open Sci 3(7):25–3622. Gutman D, Codella NCF, Celebi E, Helba B, Marchetti M, Mishra N, Halpern A (2016) 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)PublicationORIGINALMethod for the recovery of images in databases of skin cancer.pdfMethod for the recovery of images in databases of skin cancer.pdfapplication/pdf113599https://repositorio.cuc.edu.co/bitstreams/0240229c-9739-4a2b-9f1b-f3eac42fcb22/download85ca92adf87143320121054201877934MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/5f168fcc-e555-4021-91e5-e7b87b3f038c/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/e66d2bff-adb3-497b-bf80-da6eaeb5e924/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILMethod for the recovery of images in databases of skin cancer.pdf.jpgMethod for the recovery of images in databases of skin cancer.pdf.jpgimage/jpeg27680https://repositorio.cuc.edu.co/bitstreams/b292d9f2-8426-456d-860e-f1675f4d9944/download735d47a17c1bfdb0a02b78f962a513a5MD54TEXTMethod for the recovery of images in databases of skin cancer.pdf.txtMethod for the recovery of images in databases of skin cancer.pdf.txttext/plain970https://repositorio.cuc.edu.co/bitstreams/5a6d6668-a069-4412-874e-01c66f7ba430/download1a519db24b2405ed2a24781dee600558MD5511323/7708oai:repositorio.cuc.edu.co:11323/77082024-09-17 14:15:34.045http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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