{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T10:29:12Z","timestamp":1745836152405,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031160134"},{"type":"electronic","value":"9783031160141"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16014-1_23","type":"book-chapter","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T23:03:09Z","timestamp":1663714989000},"page":"283-296","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Design and\u00a0Compression Study for\u00a0Convolutional Neural Networks Based on\u00a0Evolutionary Optimization for\u00a0Thoracic X-Ray Image Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7442-8972","authenticated-orcid":false,"given":"Hassen","family":"Louati","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7088-3919","authenticated-orcid":false,"given":"Ali","family":"Louati","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1378-7415","authenticated-orcid":false,"given":"Slim","family":"Bechikh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9225-884X","authenticated-orcid":false,"given":"Lamjed","family":"Ben Said","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,21]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR2","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs\/1409.1556 (2014)"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR4","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","volume":"63","author":"S Kiranyaz","year":"2016","unstructured":"Kiranyaz, S., Ince, T., Gabbouj, M.: Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans. Biomed. Eng. 63, 664\u2013675 (2016). https:\/\/doi.org\/10.1109\/TBME.2015.2468589","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3462\u20133471 (2017)","DOI":"10.1109\/CVPR.2017.369"},{"key":"23_CR6","unstructured":"Islam, M.T., Aowal, M.A., Minhaz, A.T., Ashraf, K.: Abnormality detection and localization in chest X-rays using deep convolutional neural networks. CoRR, vol. abs\/1705.09850 (2017)"},{"issue":"11","key":"23_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pmed.1002686","volume":"15","author":"P Rajpurkar","year":"2018","unstructured":"Rajpurkar, P., et al.: Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 15(11), 1\u201317 (2018)","journal-title":"PLoS Med."},{"key":"23_CR8","unstructured":"Yao, L., Poblenz, E., Dagunts, D., Covington, B., Bernard, D., Lyman, K.: Learning to diagnose from scratch by exploiting dependencies among labels. CoRR, vol. abs\/1710.1050 (2017)"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Irvin, J., et al.: A large chest radiograph dataset with uncertainty labels and expert comparison. In: Thirty-Third AAAI Conference on Artificial Intelligence, pp. 590\u2013597 (2019)","DOI":"10.1609\/aaai.v33i01.3301590"},{"issue":"4","key":"23_CR10","first-page":"643","volume":"5","author":"PK Sethy","year":"2020","unstructured":"Sethy, P.K., Behera, S.K.: Detection of coronavirus disease (Covid-19) based on deep features. Int. J. Math. Eng. Manage. Sci. 5(4), 643\u2013651 (2020)","journal-title":"Int. J. Math. Eng. Manage. Sci."},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Luo, J., Wu, J., Lin, W.: ThiNet: a filter level pruning method for deep neural network compression. arXiv preprint arXiv: 1707.06342 (2017)","DOI":"10.1109\/ICCV.2017.541"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: International Conference on Computer Vision (ICCV), vol. 2, p. 6 (2017)","DOI":"10.1109\/ICCV.2017.155"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: International Conference on Computer Vision (ICCV), pp. 2755\u20132763 (2017)","DOI":"10.1109\/ICCV.2017.298"},{"key":"23_CR14","unstructured":"Hu, H., Peng, R., Tai, Y., Tang, C.: Network trimming: a datadriven neuron pruning approach towards efficient deep architectures. arXiv preprint arXiv: 1607.03250 (2016)"},{"key":"23_CR15","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings CVPR, pp. 2704\u20132713 (2018)","DOI":"10.1109\/CVPR.2018.00286"},{"key":"23_CR17","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. In: ICLR (2016)"},{"key":"23_CR18","unstructured":"Schmidhuber, J., Heil, S.: Predictive coding with neural nets: application to text compression. In: NeurIPS, pp. 1047\u20131054 (1995)"},{"key":"23_CR19","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural network with pruning, trained quantization and Huffman coding. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2\u20134 May 2016. Conference Track Proceedings (2016)"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Ge, S., Luo, Z., Zhao, S., Jin, X., Zhang, X.-Y.: Compressing deep neural networks for efficient visual inference. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 667\u2013672. IEEE (2017)","DOI":"10.1109\/ICME.2017.8019465"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Elias, P.: Universal codeword sets and representations of the integers. IEEE Trans. Inf. Theor. 21(2), 194\u2013203 (1975)","DOI":"10.1109\/TIT.1975.1055349"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Gallager, R., van Voorhis, D.: Optimal source codes for geometrically distributed integer alphabets. IEEE Trans. Infor. Theor. 21(2), 228\u2013230 (1975). https:\/\/doi.org\/10.1109\/TIT.1975.1055357","DOI":"10.1109\/TIT.1975.1055357"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Louati, H., Bechikh, S., Louati, A., Hung, C.-C., Said, L.B.: Deep convolutional neural network architecture design as a bi-level optimization problem. Neurocomputing 439, 44\u201362 (2021)","DOI":"10.1016\/j.neucom.2021.01.094"},{"key":"23_CR24","unstructured":"Blog, G.R.: AutoML for large scale image classification and object detection. Google Research (2017). https:\/\/researchgoogleblog.com\/2017\/11\/automl-for-large-scaleimage.html"},{"key":"23_CR25","doi-asserted-by":"publisher","unstructured":"Liang, J., Meyerson, E., Hodjat, B., Fink, D., Mutch, K., Miikkulainen, R.: Evolutionary neural AutoML for deep learning (2019). https:\/\/doi.org\/10.1145\/3321707.3321721","DOI":"10.1145\/3321707.3321721"},{"key":"23_CR26","unstructured":"Lu, Z., et al.: Multi-criterion evolutionary design of deep convolutional neural networks. ArXiv, abs\/1912.01369 (2019)"},{"key":"23_CR27","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-3-030-79457-6_11","volume-title":"Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices","author":"H Louati","year":"2021","unstructured":"Louati, H., Bechikh, S., Louati, A., Aldaej, A., Said, L.B.: Evolutionary optimization of convolutional neural network architecture design for thoracic X-ray image classification. In: Fujita, H., Selamat, A., Lin, J.C.-W., Ali, M. (eds.) IEA\/AIE 2021. LNCS (LNAI), vol. 12798, pp. 121\u2013132. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-79457-6_11"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Shinozaki, T., Watanabe, S.: Structure discovery of deep neural network based on evolutionary algorithms. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4979\u20134983 (2015)","DOI":"10.1109\/ICASSP.2015.7178918"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Dollar, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: IEEE conference on Computer Vision and Pattern Recognition, pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"issue":"2","key":"23_CR30","first-page":"1242","volume":"33","author":"Y Sun","year":"2019","unstructured":"Sun, Y., Xue, B., Zhang, M., Yen, G.G.: Completely automated CNN architecture design based on blocks. IEEE Trans. Neural Netw. Learn. Syst. 33(2), 1242\u20131254 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Lu, Z., et al.: NSGA-Net: neural architecture search using multi-objective genetic algorithm. In: Genetic and Evolutionary Computation Conference, pp. 419\u2013427 (2019)","DOI":"10.1145\/3321707.3321729"},{"issue":"11","key":"23_CR32","doi-asserted-by":"publisher","first-page":"5611","DOI":"10.1007\/s12652-020-01921-3","volume":"11","author":"A Louati","year":"2020","unstructured":"Louati, A., Louati, H., Nusir, M., hardjono, B.: Multi-agent deep neural networks coupled with LQF-MWM algorithm for traffic control and emergency vehicles guidance. J. Ambient. Intell. Humaniz. Comput. 11(11), 5611\u20135627 (2020). https:\/\/doi.org\/10.1007\/s12652-020-01921-3","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"issue":"5","key":"23_CR33","doi-asserted-by":"publisher","first-page":"4389","DOI":"10.1007\/s11227-020-03435-3","volume":"77","author":"A Louati","year":"2020","unstructured":"Louati, A., Louati, H., Li, Z.: Deep learning and case-based reasoning for predictive and adaptive traffic emergency management. J. Supercomput. 77(5), 4389\u20134418 (2020). https:\/\/doi.org\/10.1007\/s11227-020-03435-3","journal-title":"J. Supercomput."},{"issue":"8","key":"23_CR34","doi-asserted-by":"publisher","first-page":"5675","DOI":"10.1007\/s10462-020-09831-8","volume":"53","author":"A Louati","year":"2020","unstructured":"Louati, A.: A hybridization of deep learning techniques to predict and control traffic disturbances. Artif. Intell. Rev. 53(8), 5675\u20135704 (2020). https:\/\/doi.org\/10.1007\/s10462-020-09831-8","journal-title":"Artif. Intell. Rev."},{"key":"23_CR35","doi-asserted-by":"crossref","unstructured":"Louati, H., et al.: Joint design and compression of convolutional neural networks as a Bi-level optimization problem. Neural Comput. Appl. 34, 15007\u201315029 (2022). https:\/\/doi.org\/10.1007\/s00521-022-07331-0","DOI":"10.1007\/s00521-022-07331-0"}],"container-title":["Lecture Notes in Computer Science","Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16014-1_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T12:07:19Z","timestamp":1678363639000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16014-1_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031160134","9783031160141"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16014-1_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}