Abstract
Computer Vision has lately shown progress in addressing a variety of complex health care difficulties and has the potential to aid in the battle against certain lung illnesses, including COVID-19. Indeed, chest X-rays are one of the most commonly performed radiological techniques for diagnosing a range of lung diseases. Therefore, deep learning researchers have suggested that computer-aided diagnostic systems be built using deep learning methods. In fact, there are several CNN structures described in the literature. However, there are no guidelines for designing and compressing a specific architecture for a specific purpose; thus, such design remains highly subjective and heavily dependent on data scientists’ knowledge and expertise. While deep convolutional neural networks have lately shown their ability to perform well in classification and dimension reduction tasks, the challenge of parameter selection is critical for these networks. However, since a CNN has a high number of parameters, its implementation in storage devices is difficult. This is due to the fact that the search space grows exponentially in size as the number of layers increases, and the large number of parameters necessitates extensive computation and storage, making it impractical for use on low-capacity devices. Motivated by these observations, we propose an automated method for CNN design and compression based on an evolutionary algorithm (EA) for X-Ray image classification that is capable of classifying radiography images and detecting possible chest abnormalities and infections, including COVID-19.Our evolutionary method is validated through a series of comparative experiments against relevant state-of-the-art architectures.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kiranyaz, S., Ince, T., Gabbouj, M.: Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans. Biomed. Eng. 63, 664–675 (2016). https://doi.org/10.1109/TBME.2015.2468589
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. 3462–3471 (2017)
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)
Rajpurkar, P., et al.: Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 15(11), 1–17 (2018)
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)
Irvin, J., et al.: A large chest radiograph dataset with uncertainty labels and expert comparison. In: Thirty-Third AAAI Conference on Artificial Intelligence, pp. 590–597 (2019)
Sethy, P.K., Behera, S.K.: Detection of coronavirus disease (Covid-19) based on deep features. Int. J. Math. Eng. Manage. Sci. 5(4), 643–651 (2020)
Luo, J., Wu, J., Lin, W.: ThiNet: a filter level pruning method for deep neural network compression. arXiv preprint arXiv: 1707.06342 (2017)
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)
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. 2755–2763 (2017)
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)
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)
Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings CVPR, pp. 2704–2713 (2018)
Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. In: ICLR (2016)
Schmidhuber, J., Heil, S.: Predictive coding with neural nets: application to text compression. In: NeurIPS, pp. 1047–1054 (1995)
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, 2–4 May 2016. Conference Track Proceedings (2016)
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. 667–672. IEEE (2017)
Elias, P.: Universal codeword sets and representations of the integers. IEEE Trans. Inf. Theor. 21(2), 194–203 (1975)
Gallager, R., van Voorhis, D.: Optimal source codes for geometrically distributed integer alphabets. IEEE Trans. Infor. Theor. 21(2), 228–230 (1975). https://doi.org/10.1109/TIT.1975.1055357
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, 44–62 (2021)
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
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
Lu, Z., et al.: Multi-criterion evolutionary design of deep convolutional neural networks. ArXiv, abs/1912.01369 (2019)
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. 121–132. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79457-6_11
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. 4979–4983 (2015)
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. 1492–1500 (2017)
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), 1242–1254 (2019)
Lu, Z., et al.: NSGA-Net: neural architecture search using multi-objective genetic algorithm. In: Genetic and Evolutionary Computation Conference, pp. 419–427 (2019)
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), 5611–5627 (2020). https://doi.org/10.1007/s12652-020-01921-3
Louati, A., Louati, H., Li, Z.: Deep learning and case-based reasoning for predictive and adaptive traffic emergency management. J. Supercomput. 77(5), 4389–4418 (2020). https://doi.org/10.1007/s11227-020-03435-3
Louati, A.: A hybridization of deep learning techniques to predict and control traffic disturbances. Artif. Intell. Rev. 53(8), 5675–5704 (2020). https://doi.org/10.1007/s10462-020-09831-8
Louati, H., et al.: Joint design and compression of convolutional neural networks as a Bi-level optimization problem. Neural Comput. Appl. 34, 15007–15029 (2022). https://doi.org/10.1007/s00521-022-07331-0
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Louati, H., Louati, A., Bechikh, S., Ben Said, L. (2022). Design and Compression Study for Convolutional Neural Networks Based on Evolutionary Optimization for Thoracic X-Ray Image Classification. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-16014-1_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16013-4
Online ISBN: 978-3-031-16014-1
eBook Packages: Computer ScienceComputer Science (R0)