Abstract
Due to its rapidly advancing spread, the world is still reeling from COVID-19 (coronavirus 2019), which is categorized as a highly infectious disease. An early diagnosis is very critical in treating COVID-19 patients due to its lethal implications. However, the shortage of X-ray machines has resulted in life-threatening conditions and delays in diagnosis, increasing the number of deaths around the world. Therefore, in order to avoid such fatalities, COVID-19 has to be detected earlier and diagnosed faster using an intelligent computer-aided diagnosis system than with traditional screening programs. We present a novel framework for COVID-19 image categorization in this article that utilizes deep learning (DL) and bio-inspired optimization techniques. A bio-heuristic optimizer algorithm MoFAL is utilized as a feature selector to decrease the dimensionality of the image representation and increase the accuracy of the classification by ensuring that only the most essential selected features are used. Furthermore, the feature extraction is realized using the MobileNetV3 DL model. The experimental results deduced indicate that our proposed approach drastically improves performance in terms of classification accuracy and reduction in dimensions reflected during the period of feature extraction and its phases of selection. We propose that our COVID-Classifier can be deployed in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.
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References
Ahmed, Z., Mohamed, K., Zeeshan, S., Dong, X.: Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database 2020, January 2020
Bernheim, A., Mei, X., Huang, M., Yang, Y., Fayad, Z.A., Zhang, N.: Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology 295(3), 200463 (2020)
Boberg-Fazlic, N., Lampe, M., Pedersen, M., Sharp, P.: Pandemics and protectionism: evidence from the “Spanish’’ flu. Humanit. Soc. Sci. Commun. 8, 1–9 (2021)
Canayaz, M.: MH-COVIDNet: diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on x-ray images. Biomed. Sig. Process. Control 64, 102257 (2021)
Chaddad, A., Hassan, L., Desrosiers, C.: Deep CNN models for predicting COVID-19 in CT and x-ray images. J. Med. Imaging (Bellingham) 8(S1), 014502 (2021)
Ciga, O., Xu, T., Nofech-Mozes, S., Noy, S., Lu, F.I., Martel, A.L.: Overcoming the limitations of patch-based learning to detect cancer in whole slide images. Sci. Rep. 11(1), 8894 (2021)
Dai, W.C., et al.: CT imaging and differential diagnosis of COVID-19. Can. Assoc. Radiol. J. 71, 195–200 (2020)
Demirci, N.Y., et al.: Relationship between chest computed tomography findings and clinical conditions of coronavirus disease (COVID-19): a multicentre experience. Int. J. Clin. Pract. 75(9), e14459 (2021)
Elaziz, M.A., et al.: An improved marine predators algorithm with fuzzy entropy for multi-level thresholding: real world example of COVID-19 CT image segmentation. IEEE Access 8, 125306–125330 (2020)
Harjoseputro, Y., Yuda, I.P., Danukusumo, K.P.: MobileNets: efficient convolutional neural network for identification of protected birds. Int. J. Adv. Sci. Eng. Inf. Technol. 10(6), 2290 (2020)
Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.C.: Searching for MobileNetV3. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, October 2019
Huang, G., Liu, Z., Maaten, L.V.D., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269. IEEE, July 2017
Huang, X., Zeng, X., Han, R.: Dynamic inertia weight binary bat algorithm with neighborhood search. Comput. Intell. Neurosci. 2017, 1–15 (2017). https://doi.org/10.1155/2F2017/2F3235720
Ignatov, A., et al.: Real-time video super-resolution on smartphones with deep learning, mobile AI 2021 challenge: report. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, June 2021
Ji, J., Krishna, R., Fei-Fei, L., Niebles, J.C.: Action genome: actions as compositions of spatio-temporal scene graphs. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, June 2020
Kesim, E., Dokur, Z., Olmez, T.: X-ray chest image classification by a small-sized convolutional neural network. In: 2019 Scientific Meeting on Electrical-Electronics Biomedical Engineering and Computer Science (EBBT), pp. 1–5 (2019)
Khuzani, A.Z., Heidari, M., Shariati, S.A.: COVID-classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images. Sci. Rep. 11(1), 9887 (2021)
Kundu, R., Basak, H., Singh, P.K., Ahmadian, A., Ferrara, M., Sarkar, R.: Fuzzy rank-based fusion of CNN models using gompertz function for screening COVID-19 CT-scans. Sci. Rep. 11(1), 14133 (2021)
Larici, A.R., Cicchetti, G., Marano, R., Bonomo, L., Storto, M.L.: COVID-19 pneumonia: current evidence of chest imaging features, evolution and prognosis. Chin. J. Acad. Radiol. 4, 229–240 (2021)
Liu, J., Inkawhich, N., Nina, O., Timofte, R.: NTIRE 2021 multi-modal aerial view object classification challenge. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 588–595, June 2021
Maior, C.B.S., Santana, J.M.M., Lins, I.D., Moura, M.J.C.: Convolutional neural network model based on radiological images to support COVID-19 diagnosis: evaluating database biases. PLoS ONE 16(3), e0247839 (2021)
Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl. Based Syst. 89, 228–249 (2015). https://doi.org/10.1016/2Fj.knosys.2015.07.006
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016). https://doi.org/10.1016/j.advengsoft.2016.01.008
Narin, A.: Accurate detection of COVID-19 using deep features based on x-ray images and feature selection methods. Comput. Biol. Med. 137, 104771 (2021)
Oh, Y., Park, S., Ye, J.C.: Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans. Med. Imaging 39(8), 2688–2700 (2020)
Onder, O., Yarasir, Y., Azizova, A., Durhan, G., Onur, M.R., Ariyurek, O.M.: Errors, discrepancies and underlying bias in radiology with case examples: a pictorial review. Insights Imaging 12(1), 51 (2021)
Ramachandran, P., Zoph, B., Le, Q.V.: Searching for activation functions. CoRR abs/1710.05941 (2017). https://arxiv.org/abs/1710.05941
Roy, S., et al.: Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Trans. Med. Imaging 39(8), 2676–2687 (2020)
Sahlol, A.T., Yousri, D., Ewees, A.A., Al-qaness, M.A.A., Damasevicius, R., Elaziz, M.A.: COVID-19 image classification using deep features and fractional-order marine predators algorithm. Sci. Rep. 10(1), 15364 (2020)
Siddavaatam, P., Sedaghat, R.: Grey wolf optimizer driven design space exploration: a novel framework for multi-objective trade-off in architectural synthesis. Swarm Evol. Comput. 49, 44–61 (2019)
Siddavaatam, P., Sedaghat, R.: A new bio-heuristic hybrid optimization for constrained continuous problems. In: Gavrilova, M.L., Tan, C.J.K. (eds.) Transactions on Computational Science XXXVIII. LNCS, vol. 12620, pp. 76–97. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-662-63170-6_5
Silverman, B.W., Jones, M.C.: E. Fix and J.L. Hodges (1951): an important contribution to nonparametric discriminant analysis and density estimation. Commentary on Fix and Hodges (1951). Int. Stat. Rev./Revue Internationale de Statistique 57(3), 233 (1989)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015)
Sun, L., Shao, W., Wang, M., Zhang, D., Liu, M.: High-order feature learning for multi-atlas based label fusion: application to brain segmentation with MRI. IEEE Trans. Image Process. 29, 2702–2713 (2020)
Tan, M., et al.: MnasNet: platform-aware neural architecture search for mobile. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, June 2019
Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, 09–15 June 2019, vol. 97, pp. 6105–6114. Proceedings of Machine Learning Research. PMLR (2019)
Tran, D., Wang, H., Feiszli, M., Torresani, L.: Video classification with channel-separated convolutional networks. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, October 2019
Wang, J., et al.: Prior-attention residual learning for more discriminative COVID-19 screening in CT images. IEEE Trans. Med. Imaging 39(8), 2572–2583 (2020)
Wang, S., et al.: A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur. Respir. J. 56(2), 2000775 (2020)
Wang, X., et al.: A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT. IEEE Trans. Med. Imaging 39(8), 2615–2625 (2020)
Yang, X.S.: Firefly algorithm, lévy flights and global optimization. In: Bramer, M., Ellis, R., Petridis, M. (eds.) Research and Development in Intelligent Systems XXVI. Springer, London (2010). https://doi.org/10.1007/978-1-84882-983-1_15
Yang, X., He, X., Zhao, J., Zhang, Y., Zhang, S., **e, P.: COVID-CT-dataset: a CT scan dataset about COVID-19 (2020)
Yousri, D., Elaziz, M.A., Abualigah, L., Oliva, D., Al-qaness, M.A., Ewees, A.A.: COVID-19 x-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions. Appl. Soft Comput. 101, 107052 (2021)
Zhang, K., et al.: Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 181(6), 1423.e11–1433.e11 (2020)
Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, June 2018
Zhu, Z., Lian, X., Su, X., Wu, W., Marraro, G., Zeng, Y.: From SARS and MERS to COVID-19: a brief summary and comparison of severe acute respiratory infections caused by three highly pathogenic human coronaviruses. Respir. Res. 21, 224 (2020)
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, June 2018
Acknowledgement
I would like to acknowledge my late mother Ms. Meena Ramaiah, who inspired me to research on COVID-19 in this article.
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Siddavaatam, P., Sedaghat, R., Sahelgozin, N. (2022). A Novel Machine Learning Framework for Covid-19 Image Classification with Bio-heuristic Optimization. In: Gavrilova, M.L., Tan, C.J.K. (eds) Transactions on Computational Science XXXIX. Lecture Notes in Computer Science(), vol 13460. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-66491-9_5
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