{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:23:59Z","timestamp":1743085439189,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031426155"},{"type":"electronic","value":"9783031426162"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-42616-2_13","type":"book-chapter","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T06:02:01Z","timestamp":1693461721000},"page":"173-186","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Designing Attention Based Convolutional Neural Network (CNN) Architectures for\u00a0Medical Image Classification Using Genetic Algorithm Based on\u00a0Variable Length-Encoding Scheme"],"prefix":"10.1007","author":[{"given":"Muhammad Junaid","family":"Ali","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laurent","family":"Moalic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mokhtar","family":"Essaid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lhassane","family":"Idoumghar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,1]]},"reference":[{"key":"13_CR1","first-page":"2018","volume":"1","author":"A Voulodimos","year":"2018","unstructured":"Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E.: Deep learning for computer vision: a brief review. Comput. Intell. Neurosci. 1, 2018 (2018)","journal-title":"Comput. Intell. Neurosci."},{"issue":"42","key":"13_CR2","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"1","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 1(42), 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"key":"13_CR3","unstructured":"Yang, J., et al.: MedMNIST v2: A large-scale lightweight benchmark for 2d and 3d biomedical image classification. arXiv preprint arXiv:2110.14795. 2021 Oct 27"},{"key":"13_CR4","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/TNNLS.2021.3100554","volume":"34","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Sun, Y., Xue, B., Zhang, M., Yen, G.G., Tan, K.C.: A survey on evolutionary neural architecture search. IEEE Trans. Neural Netw. Learn. Syst. 34, 550\u2013570 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Yotchon, P., Jewajinda, Y.: Hybrid multi-population evolution based on genetic algorithm and regularized evolution for neural architecture search. In: 2020 17th International Joint Conference on Computer Science and Software Engineering (JCSSE) 2020 Nov 4, pp. 183\u2013187. IEEE","DOI":"10.1109\/JCSSE49651.2020.9268416"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Rakhshani, H., et al.: Neural architecture search for time series classification. In: 2020 International Joint Conference on Neural Networks (IJCNN), 2020 July 19, pp. 1\u20138. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9206721"},{"issue":"7","key":"13_CR7","doi-asserted-by":"publisher","first-page":"44247","DOI":"10.1109\/ACCESS.2019.2908991","volume":"4","author":"Y Weng","year":"2019","unstructured":"Weng, Y., Zhou, T., Li, Y., Qiu, X.: NAS-UNET: neural architecture search for medical image segmentation. IEEE Access 4(7), 44247\u201357 (2019)","journal-title":"IEEE Access"},{"issue":"9","key":"13_CR8","doi-asserted-by":"publisher","first-page":"3840","DOI":"10.1109\/TCYB.2020.2983860","volume":"50","author":"Y Sun","year":"2020","unstructured":"Sun, Y., Xue, B., Zhang, M., Yen, G.G., Lv, J.: Automatically designing CNN architectures using the genetic algorithm for image classification. IEEE Trans. Cybern. 50(9), 3840\u201354 (2020)","journal-title":"IEEE Trans. Cybern."},{"key":"13_CR9","unstructured":"Zoph B, Le QV. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578. 5 November 2016"},{"key":"13_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1007\/978-3-030-29894-4_52","volume-title":"PRICAI 2019: Trends in Artificial Intelligence","author":"B Wang","year":"2019","unstructured":"Wang, B., Sun, Y., Xue, B., Zhang, M.: A hybrid GA-PSO method for evolving architecture and short connections of deep convolutional neural networks. In: Nayak, A.C., Sharma, A. (eds.) PRICAI 2019. LNCS (LNAI), vol. 11672, pp. 650\u2013663. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29894-4_52"},{"issue":"362","key":"13_CR11","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.neucom.2019.07.026","volume":"14","author":"Y Li","year":"2019","unstructured":"Li, Y., Xiao, J., Chen, Y., Jiao, L.: Evolving deep convolutional neural networks by quantum behaved particle swarm optimization with binary encoding for image classification. Neurocomputing 14(362), 156\u2013165 (2019)","journal-title":"Neurocomputing"},{"key":"13_CR12","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"13_CR13","unstructured":"Guo, M.H., et al.: Attention mechanisms in computer vision: a survey. arXiv preprint arXiv:2111.07624. 15 November 2021"},{"issue":"239","key":"13_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107968","volume":"5","author":"Z Fan","year":"2022","unstructured":"Fan, Z., Hu, G., Sun, X., Wang, G., Dong, J., Su, C.: Self-attention neural architecture search for semantic image segmentation. Knowl. Based Syst. 5(239), 107968 (2022)","journal-title":"Knowl. Based Syst."},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016, pp. 770\u2013778 (2017)","DOI":"10.1109\/CVPR.2016.90"},{"key":"13_CR16","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1007\/978-3-030-31129-2_43","volume-title":"Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019","author":"AAF Ahmed","year":"2020","unstructured":"Ahmed, A.A.F., Darwish, S.M.S., El-Sherbiny, M.M.: A novel automatic CNN architecture design approach based on genetic algorithm. In: Hassanien, A.E., Shaalan, K., Tolba, M.F. (eds.) AISI 2019. AISC, vol. 1058, pp. 473\u2013482. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-31129-2_43"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Johnson, F., Valderrama, A., Valle, C., Crawford, B., Soto, R., Nanculef, R.: Automating configuration of convolutional neural network hyperparameters using genetic algorithm. IEEE Access. 25(8), 156139\u2013156152 (2020)","DOI":"10.1109\/ACCESS.2020.3019245"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"da Silva, C.A., Miranda, P.B., Cordeiro, F.R.: A new grammar for creating convolutional neural networks applied to medical image classification. In: 2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2021 October 18, pp. 97\u2013104. IEEE (2021)","DOI":"10.1109\/SIBGRAPI54419.2021.00022"},{"key":"13_CR19","first-page":"20309","volume":"33","author":"C White","year":"2020","unstructured":"White, C., Neiswanger, W., Nolen, S., Savani, Y.: A study on encodings for neural architecture search. Adv. Neural. Inf. Process. Syst. 33, 20309\u201320319 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"31","key":"13_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2020.105928","volume":"1","author":"ML Huang","year":"2020","unstructured":"Huang, M.L., Lin, T.Y.: Dataset of breast mammography images with masses. Data Brief 1(31), 105928 (2020)","journal-title":"Data Brief"},{"key":"13_CR21","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Jin, H., Song, Q., Hu, X.: Auto-keras: an efficient neural architecture search system. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019 July 25, pp. 1946\u20131956 (2019)","DOI":"10.1145\/3292500.3330648"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision 2017, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"issue":"10","key":"13_CR24","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 11531\u201311539 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Back, T.: Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, 11 January 1996","DOI":"10.1093\/oso\/9780195099713.001.0001"}],"container-title":["Lecture Notes in Computer Science","Artificial Evolution"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-42616-2_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T06:03:56Z","timestamp":1693461836000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42616-2_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031426155","9783031426162"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42616-2_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Evolution (Evolution Artificielle)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Exeter","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ae2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ea2022.inria.fr\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"https:\/\/sciencesconf.org\/","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"83% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}