Anales de la RANM

273 A N A L E S R A N M R E V I S T A F U N D A D A E N 1 8 7 9 DEEP LEARNING GENITAL LESIONS IMAGE CLASSIFICATION González-Alday R, et al. An RANM. 2022;139(03): 266 - 273 CONFLICT OF INTEREST STATEMENT The authors of this article declare that they have no conflict of interest with respect to what is expressed in this work. BIBLIOGRAPHY 1. World Health Organization. Sexually trans- mitted infections (STIs). Fact Sheets [Inter- net]. 2019 [cited 2022 Jun 20]; Available from: https://www.who.int/news-room/fact-sheets/ detail/sexually-transmitted-infections-(stis) 2. Chesson HW, Mayaud P, Aral SO. Sexually transmitted infections: Impact and cost-effecti- veness of prevention. En: Holmes KK, Bertozzi S, Bloom BR, Jha P, editors. Major infectious diseases. 3rd ed. Washington (DC): The Inter- national Bank for Reconstruction and Develop- ment / The World Bank; 2017. 3. Unemo M, Bradshaw CS, Hocking JS et al. Se- xually transmitted infections: challenges ahead. Lancet Infect Dis. 2017; 17(8): e235-279. 4. Williamson DA, Chen MY. Emerging and ree- merging sexually transmitted infections. New Engl J Med. 2020; 382(21): 2023-2032. 5. Centers for disease control and prevention. Std Fact Sheets - Genital herpes [Internet]. 2022 [cited 2022 Jul 11]. Available from: https:// www.cdc.gov/std/herpes/stdfact-herpes.htm 6. Kimberlin DW, Rouse DJ. Genital herpes. New Engl J Med. 2004; 350(19): 1970-1977. 7. Centers for disease control and prevention. Std Fact Sheets - Human papillomavirus (HPV) [In- ternet]. 2022 [cited 2022 Jul 11]. Available from: https://www.cdc.gov/std/hpv/stdfact-hpv.htm 8. Sejnowski TJ. The deep learning revolution. MIT press; 2018. 9. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015; 521(7553): 436-444. 10. Goodfellow I, Bengio Y, Courville A. Deep lear- ning. MIT press; 2016. 11. Esteva A, Robicquet A, Ramsundar B et al. A guide to deep learning in healthcare. Nat Med. 2019; 25(1): 24-29. 12. Gu J, Wang Z, Kuen J et al. Recent advances in convolutional neural networks. Pattern Recogn. 2018; 77: 354-377. 13. Albawi S, Mohammed TA, Al-Zawi S. Unders- tanding of a convolutional neural network. En: 2017 international conference on engineering and technology (ICET). IEEE; 2017. p. 1-6. 14. Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. En: International conference on machine learning. PMLR. 2019. p. 6105-6114. 15. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. Imagenet: a large-scale hierarchical image database. En: 2009 IEEE conference on com- puter vision and pattern recognition. 2009. p. 248-255. 16. Pan SJ, Yang Q. A survey on transfer learning. IEEE Transactions on knowledge and data engi- neering. 2009; 22(10): 1345-1359. 17. Shorten C, Khoshgoftaar TM. A survey on ima- ge data augmentation for deep learning. Journal of big data. 2019; 6(1): 1-48. 18. Adadi A, Berrada M. Peeking inside the black- box: a survey on explainable artificial intelli- gence (XAI). IEEE access. 2018; 6: 52138-52160. 19. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: Visual explana- tions from deep networks via gradient-based localization. En: Proceedings of the IEEE inter- national conference on computer vision. 2017. p. 618-626. Si desea citar nuestro artículo: González-Alday R, Peinado F, Carrillo D, Maojo V. Deep Learning for Clinical Image Classification of Genital Lesions Caused by Se- xually Transmitted Diseases. An RANM. 2022;139(03): 266– 273. DOI: 10.32440/ar.2022.139.03. rev07