Abstract
Computer Vision, as an area of Artificial Intelligence, has recently achieved success in tackling numerous difficult challenges in health care and has the potential to contribute to the fight against several lung diseases, including COVID-19. In fact, a chest X-ray is one of the most frequent radiological procedures used to diagnose a variety of lung illnesses. Therefore, deep learning researchers have recommended that deep learning techniques can be used to build computer-aided diagnostic systems. According to the literature, there are a variety of CNN structures. Unfortunately, there are no guidelines for compressing these architectural designs for any particular task. For these reasons, this design is still very subjective and hugely dependent on data scientists’ expertise. Deep convolution neural networks have recently proven their capacity to perform well in classification and dimension reduction tasks. However, the problem of selecting hyper-parameters is essential for these networks. This is due to the fact that the size of the search space rises exponentially with the number of layers, and the large number of parameters requires extensive calculations and storage, which makes it unsuitable for application in low-capacity devices. In this paper, we present a system based on a genetic method for compressing CNNs to classify radiographic images and detect the possible thoracic anomalies and infections, including the case of COVID-19. This system uses pruning, quantization, and compression approaches to minimize the network complexity of various CNNs while maintaining good accuracy. The suggested technique combines the use of genetic algorithms (GAs) to execute convolutional layer pruning selection criteria. Our suggested system is validated by a series of comparison experiments and tests with regard to relevant state-of-the-art architectures used for thoracic X-ray image classification.
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References
Paules, C.I., Marston, H.D., Fauci, A.S.: Coronavirus infections-more than just the common cold. JAMA 323(8), 707–708 (2020)
Chen, Y., Liu, Q., Guo, D.: Emerging coronaviruses: genome structure, replication, and pathogenesis. J. Med. Virol. 92(4), 418–423 (2020)
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., 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
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.: Chexpert: 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. Manag. 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)
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)
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, A., Lahyani, R., Aldaej, A., Mellouli, R., Nusir, M.: Mixed integer linear programming models to solve a real-life vehicle routing problem with pickup and delivery. Appl. Sci. 11(20), 9551 (2021). https://doi.org/10.3390/app11209551
Louati, A., Lahyani, R., Aldaej, A., Aldumaykhi, A., Otai, S.: Price forecasting for real estate using machine learning: a case study on Riyadh city. Concurr. Computa. Pract. Exp. 34(6), e6748 (2022). https://doi.org/10.1002/cpe.6748
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)
Chauhan, J., Rajasegaran, J., Seneviratne, S., Misra, A., Seneviratne, A., Lee, Y.: Performance characterization of deep learning models for breathing-based authentication on resource-constrained devices. In: Proceedings of IMWUT, vol. 2, no. 4, pp. 1–24 (2018)
Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of 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 (2016b)
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)
Bechikh, S., Said, L.B., Ghédira, K.: Negotiating decision makers’ reference points for group preference-based Evolutionary multi-objective Optimization. In: 11th International Conference on Hybrid Intelligent Systems (HIS), pp. 377–382 (2011). https://doi.org/10.1109/HIS.2011.6122135
Gallager, R., van Voorhis, D.: Optimal source codes for geometrically distributed integer alphabets (Corresp.). IEEE Trans. Inf. Theory 21(2), 228–230 (1975). https://doi.org/10.1109/TIT.1975.1055357
Louati, H., Bechikh, S., Louati, A., Aldaej, A., Said, L.B.: Evolutionary optimization of convolutional neural network architecture design for thoracic xray image classification. In: IEA/AIE, vol. 32, no. 1 (2021)
Louati, H., Bechikh, S., Louati, A., Hung, C.-C., Said, L.B.: Deep convolutional neural network architecture design as a bi-level optimization problem. 655 Neurocomput. 439, 44–62 (2021)
Rahul, M., Gupta, H.P., Dutta, T.: A survey on deep neural network compression: challenges, overview, and solutions. arXiv, arXiv:2010.03954 (2020)
Acknowledgements
This project was supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University through the research project No. 2022/01/19780.
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Louati, H., Bechikh, S., Louati, A., Aldaej, A., Said, L.B. (2022). Evolutionary Optimization for CNN Compression Using Thoracic X-Ray Image Classification. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_10
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