Research article

CNGOD-An improved convolution neural network with grasshopper optimization for detection of COVID-19


  • Received: 12 June 2022 Revised: 27 July 2022 Accepted: 12 August 2022 Published: 26 August 2022
  • The world is facing the pandemic situation due to a beta corona virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease caused by this virus known as Corona Virus Disease 2019 (COVID-19) has affected the entire world. The current diagnosis methods are laboratory based and require specialized testing kits for performing the test. Therefore, to overcome the limitations of testing kits a diagnosis method from chest X-ray images is proposed in this paper. Chest X-ray images can be easily obtained by X-ray machines that are readily available at medical centres. The radiological examinations augmented with chest X-ray images is an effective way of disease diagnosis. The automated analysis of the chest X-ray images requires a highly efficient method for identifying COVID-19 from these images. Thus, a novel deep convolution neural network (CNN) optimized using Grasshopper Optimization Algorithm (GOA) is proposed. The deep learning model comprises depth wise separable convolutions that independently look at cross channel and spatial correlations. The optimization of deep learning models is a complex task due the multiple layers and their non-linearities. In image classification problems optimizers like Adam, SGD etc. get stuck in local minima. Thus, in this paper a metaheuristic optimization algorithm is used to optimize the network. Grasshoper Optimization Algorithm (GOA) is a metaheuristic algorithm that mimics the behaviour of grasshoppers for food search. This algorithm is a fast converging and is capable of exploration and exploitation of large search spaces. Maximum Probability Based Cross Entropy Loss (MPCE) loss function is used as it minimizes the back propogation error of cross entropy and improves the training. The experimental results show that the proposed method gives high classification accuracy. The interpretation of results is augmented with class activation maps. Grad-CAM visualization algorithm is used for class activation maps.

    Citation: Akansha Singh, Krishna Kant Singh, Michal Greguš, Ivan Izonin. CNGOD-An improved convolution neural network with grasshopper optimization for detection of COVID-19[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 12518-12531. doi: 10.3934/mbe.2022584

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  • The world is facing the pandemic situation due to a beta corona virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease caused by this virus known as Corona Virus Disease 2019 (COVID-19) has affected the entire world. The current diagnosis methods are laboratory based and require specialized testing kits for performing the test. Therefore, to overcome the limitations of testing kits a diagnosis method from chest X-ray images is proposed in this paper. Chest X-ray images can be easily obtained by X-ray machines that are readily available at medical centres. The radiological examinations augmented with chest X-ray images is an effective way of disease diagnosis. The automated analysis of the chest X-ray images requires a highly efficient method for identifying COVID-19 from these images. Thus, a novel deep convolution neural network (CNN) optimized using Grasshopper Optimization Algorithm (GOA) is proposed. The deep learning model comprises depth wise separable convolutions that independently look at cross channel and spatial correlations. The optimization of deep learning models is a complex task due the multiple layers and their non-linearities. In image classification problems optimizers like Adam, SGD etc. get stuck in local minima. Thus, in this paper a metaheuristic optimization algorithm is used to optimize the network. Grasshoper Optimization Algorithm (GOA) is a metaheuristic algorithm that mimics the behaviour of grasshoppers for food search. This algorithm is a fast converging and is capable of exploration and exploitation of large search spaces. Maximum Probability Based Cross Entropy Loss (MPCE) loss function is used as it minimizes the back propogation error of cross entropy and improves the training. The experimental results show that the proposed method gives high classification accuracy. The interpretation of results is augmented with class activation maps. Grad-CAM visualization algorithm is used for class activation maps.



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    [1] F. Jiang, L. Deng, L. Zhang, Y. Cai, C. W. Cheung, Z. Xia, Review of the clinical characteristics of coronavirus disease 2019 (COVID-19), J. Gen. Intern. Med., 35 (2020), 1545−1549. https://doi.org/10.1007/s11606-020-05762-w doi: 10.1007/s11606-020-05762-w
    [2] K. K. Singh, A. Singh, Diagnosis of COVID-19 from chest X-ray images using wavelets-based depthwise convolution network, Big Data Min. Anal., 4 (2021), 84−93. https://doi.org/10.26599/BDMA.2020.9020012 doi: 10.26599/BDMA.2020.9020012
    [3] J. H. Beigel, K. M. Tomashek, L. E. Dodd, Remdesivir for the treatment of COVID-19—preliminary report, N. Engl. J. Med., 383 (2020), 992−994. https://doi.org/10.1056/nejmc2022236 doi: 10.1056/NEJMc2022236
    [4] M. Jangra, S. K. Dhull, K. K. Singh, A. Singh, X. Cheng, O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification, Complex Intell. Syst., 2021 (2021), 1−14. https://doi.org/10.1007/s40747-021-00371-4 doi: 10.1007/s40747-021-00371-4
    [5] Satyender, S. Dhull, K. K. Singh, ESIMD: epileptic seizure identification using metaheuristic deep learning technique, Expert Syst., 39 (2022), e12897. https://doi.org/10.1111/exsy.12897 doi: 10.1111/exsy.12897
    [6] A. Dhull, K. Khanna, A. Singh, G. Gupta, ACO inspired computer-aided detection/diagnosis (CADe/CADx) model for medical data classification, Recent Pat. Comput. Sci., 12 (2019), 250−259. https://doi.org/10.2174/2213275912666181205155018 doi: 10.2174/2213275912666181205155018
    [7] S. Mondal, N. Mandal, A. Singh, K. K. Singh, Blood vessel detection from Retinal fundas images using GIFKCN classifier, Procedia Comput. Sci., 167 (2020), 2060−2069. https://doi.org/10.1016/j.procs.2020.03.246 doi: 10.1016/j.procs.2020.03.246
    [8] K. K. Singh, M. Siddhartha, A. Singh, Diagnosis of coronavirus disease (COVID-19) from chest X-ray images using modified XceptionNet, Rom. J. Inf. Sci. Technol., 23 (2020), 91−105. Available from: https://www.researchgate.net/publication/341966812.
    [9] C. Garbin, X. Zhu, O. Marques, Dropout vs. batch normalization: an empirical study of their impact to deep learning, Multimedia Tools Appl., 79 (2020), 12777–12815. https://doi.org/10.1007/s11042-019-08453-9 doi: 10.1007/s11042-019-08453-9
    [10] T. Ozturk, M. Talo, E. Yildirim, U. B. Baloglu, O. Yildirim, U. R. Acharya, Automated detection of COVID-19 cases using deep neural networks with X-ray images, Comput. Biol. Med., 121 (2020), 103792. https://doi.org/10.1016/j.compbiomed.2020.103792 doi: 10.1016/j.compbiomed.2020.103792
    [11] Y. Zhou, X. Wang, M. Zhang, J. Zhu, R. Zheng, Q. Wu, MPCE: a maximum probability based cross entropy loss function for neural network classification, IEEE Access, 7 (2019), 146331−146341. https://doi.org/10.1109/ACCESS.2019.2946264 doi: 10.1109/ACCESS.2019.2946264
    [12] I. Jais, A. Ismail, S. Nisa, Adam optimization algorithm for wide and deep neural network, Knowl. Eng. Data Sci., 2 (2019), 41−46. https://doi.org/10.17977/um018v2i12019p41-46 doi: 10.17977/um018v2i12019p41-46
    [13] A. C. Wilson, R. Roelofs, M. Stern, N. Srebro, B. Recht, The marginal value of adaptive gradient methods in machine learning, in Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017 (2017), 4151−4161. Available from: https://dl.acm.org/doi/10.5555/3294996.3295170.
    [14] M. Marti, S. Bujwid, A. Pieropan, H. Azizpour, A. Maki, An analysis of over-sampling labeled data in semi-supervised learning with FixMatch, in Proceedings of the Northern Lights Deep Learning Workshop, 3 (2022), 1−11. https://doi.org/10.7557/18.6269
    [15] S. Saremi, S. Mirjalili, A. Lewis, Grasshopper optimisation algorithm: theory and application, Adv. Eng. Software, 105 (2017), 30−47. https://doi.org/10.1016/j.advengsoft.2017.01.004 doi: 10.1016/j.advengsoft.2017.01.004
    [16] J. Bansal, Particle swarm optimization, Stud. Comput. Intell., 779 (2018), 11−23. https://doi.org/10.1007/978-3-319-91341-4_2 doi: 10.1007/978-3-319-91341-4_2
    [17] A. Abbas, M. Abdelsamea, M. Gaber, Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network, Appl. Intell., 51 (2020), 854−864. https://doi.org/10.1007/s10489-020-01829-7 doi: 10.1007/s10489-020-01829-7
    [18] L. Li, Y. Si, Z. Jia, Medical image enhancement based on CLAHE and unsharp masking in NSCT domain, J. Med. Imaging Health Inf., 8 (2018), 431−438. https://doi.org/10.1166/jmihi.2018.2328 doi: 10.1166/jmihi.2018.2328
    [19] A. F. Agarap, Deep learning using rectified linear units (ReLU), preprint, arXiv: 1803.08375.
    [20] J. P. Cohen, P. Morrison, L. Dao, COVID-19 image data collection, preprint, arXiv: 2003.11597.
    [21] D. Kermany, K. Zhang, M. Goldbaum, Labeled optical coherence tomography (OCT) and chest X-ray images for classification, Mendeley Data, 2 (2018). https://doi.org/10.17632/rscbjbr9sj.2 doi: 10.17632/rscbjbr9sj.2
    [22] Y. Guo, Y. Li, L. Wang, T. Rosing, Depthwise convolution is all you need for learning multiple visual domains, in Proceedings of the AAAI Conference on Artificial Intelligence, 33 (2019), 8368−8375. https://doi.org/10.1609/aaai.v33i01.33018368
    [23] A. Tharwat, Classification assessment methods, Appl. Comput. Inf., 17 (2020), 168−192. https://doi.org/10.1016/j.aci.2018.08.003 doi: 10.1016/j.aci.2018.08.003
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