{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:13:17Z","timestamp":1750219997552,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":11,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,29]]},"DOI":"10.1145\/3500931.3500965","type":"proceedings-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T05:13:28Z","timestamp":1640236408000},"page":"188-198","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Intelligent Skin Cancer Detection System Based on Convolutional Neural Networks"],"prefix":"10.1145","author":[{"given":"Yurui","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Northeastern University, Shenyang, China"}]},{"given":"Duan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Zhaoyun","family":"Xu","sequence":"additional","affiliation":[{"name":"Software Engineering, Beihang University, Beijing, China"}]},{"given":"Ziyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Texas at Austin, Austin, Texas, United States"}]}],"member":"320","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSPIS51252.2020.9340143"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICECS49266.2020.9294814"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRoM48714.2019.9071823"},{"key":"e_1_3_2_1_4_1","volume-title":"The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data, 5(1): 1--9","author":"Tschandl P.","year":"2018","unstructured":"Tschandl , P. , Rosendahl , C. and Kittler , K . ( 2018 ) The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data, 5(1): 1--9 . Tschandl, P., Rosendahl, C. and Kittler, K. (2018) The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data, 5(1): 1--9."},{"key":"e_1_3_2_1_5_1","volume-title":"Soft-Attention Improves Skin Cancer Classification Performance. arXiv preprint arXiv: 2105.03358","author":"Datta S.K","year":"2021","unstructured":"Datta , S.K , Shaikh , M.A. , Srihari , S.N. , ( 2021 ) Soft-Attention Improves Skin Cancer Classification Performance. arXiv preprint arXiv: 2105.03358 . Datta, S.K, Shaikh, M.A., Srihari, S.N., et al. (2021) Soft-Attention Improves Skin Cancer Classification Performance. arXiv preprint arXiv: 2105.03358."},{"key":"e_1_3_2_1_6_1","volume-title":"Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:1602.07261","author":"Szegedy C.","year":"2016","unstructured":"Szegedy , C. , Ioffe , S. , Vanhoucke , V. and Alemi , A . ( 2016 ) Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:1602.07261 . Szegedy, C., Ioffe, S., Vanhoucke, V. and Alemi, A. (2016) Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:1602.07261."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"e_1_3_2_1_8_1","volume-title":"Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research. 15(1):1929--1958","author":"Srivastava N.","year":"2014","unstructured":"Srivastava , N. , Hinton , G. , Krizhevsky , A. , Sutskever , I. and Salakhutdinov , R . ( 2014 ) Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research. 15(1):1929--1958 . Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. (2014) Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research. 15(1):1929--1958."},{"key":"e_1_3_2_1_9_1","volume-title":"Medium, Analytics Vidhya.","author":"Parul P.","year":"2021","unstructured":"Parul , P. ( 2021 ) Building a Simple Chatbot from Scratch in Python (Using NLTK) . In: Medium, Analytics Vidhya. Parul, P. (2021) Building a Simple Chatbot from Scratch in Python (Using NLTK). In: Medium, Analytics Vidhya."},{"key":"e_1_3_2_1_10_1","volume-title":"A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma D.P.","year":"2014","unstructured":"Kingma , D.P. and Adam , J.B . ( 2014 ) A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Kingma, D.P. and Adam, J.B. (2014) A method for stochastic optimization. arXiv preprint arXiv:1412.6980."},{"key":"e_1_3_2_1_11_1","volume-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167","author":"Ioffe S.","year":"2015","unstructured":"Ioffe , S. and Szegedy , C . ( 2015 ) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 . Ioffe, S. and Szegedy, C. (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167."}],"event":{"name":"ISAIMS 2021: 2nd International Symposium on Artificial Intelligence for Medicine Sciences","acronym":"ISAIMS 2021","location":"Beijing China"},"container-title":["Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3500931.3500965","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3500931.3500965","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:50:52Z","timestamp":1750182652000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3500931.3500965"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,29]]},"references-count":11,"alternative-id":["10.1145\/3500931.3500965","10.1145\/3500931"],"URL":"https:\/\/doi.org\/10.1145\/3500931.3500965","relation":{},"subject":[],"published":{"date-parts":[[2021,10,29]]},"assertion":[{"value":"2021-12-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}