Abstract
COVID-19, also known as Corona Virus Disease, was first discovered in a city of China named Wuhan in December 2019 and it has been announced as a global pandemic in the middle of 2020. According to experts, this virus may also infect the upper respiratory system, which includes the sinuses, nose, and throat, and the lower respiratory system, which includes the windpipe and lungs. The disease can infect other people via respiratory droplets and come near to the COVID-19 infected people and a low rate of contamination is stated through surfaces and objects touch. Nowadays, millions of people across the globe are suffering from this disease, causing a huge death rate. Even after taking serious precaution measures, the number of patients dealing with this disease and the death toll are still rising at a drastic rate. In this paper, we approach a fast and effective measure to detect COVID-19 using CT scan images. First, we collected data and classified using VGG16, VGG19, EfficientNetB0, ResNet50, and ResNet101. From our result; we got an accuracy rate of 85.33% from VGG16, 87.86% from VGG19, and 82.35% from ResNet101. Then we formed an ensemble model with these best three classifiers and achieved a best overall accuracy rate of 90.89% from COV19EXAI V1 and 91.82% from COV19EXAI V2. Finally, we integrated XAI in our model to achieve a better understanding of our classification.
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References
World Health Organization, et al.: Coronavirus disease (COVID-19) (2020)
Jacobi, A., Chung, M., Bernheim, A., Eber, C.: Portable chest x-ray in coronavirus disease-19 (COVID-19): a pictorial review. Clin. Imaging 64, 35–42 (2020)
Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Acharya, U.R.: Automated detection of COVID-19 cases using deep neural networks with x-ray images. Comput. Biol. Med. 121, 103792 (2020)
Ucar, F., Korkmaz, D.: COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnostic of the coronavirus disease: (COVID-19) from x-ray images. Med. Hypotheses 140, 109761 (2020)
Sethy, P.K., Behera, S.K.: Detection of coronavirus disease (COVID-19) based on deep features. Preprints 2020030300 (2020)
Li, L., et al. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 296 (2020)
Wang, S., et al.: A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur. Respir. J. 56 (2020)
Habibzadeh, M., Jannesari, M., Rezaei, Z., Baharvand, H., Totonchi, M.: Automatic white blood cell classification using pre-trained deep learning models: ResNet and inception. In: Tenth International Conference on Machine Vision (ICMV 2017), vol. 10696, p. 1069612. International Society for Optics and Photonics (2018)
Amma, T.A., Sunny, A.R., Biji, K.P., Mohanan, M.: Lung cancer identification and prediction based on VGG architecture. In. J. Res. Eng. Sci. Manage. 3(7), 88–92 (2020)
Müftüoğlu, Z., Kizrak, M.A., Yildlnm, T.: Differential privacy practice on diagnosis of COVID-19 radiology imaging using EfficientNet. In: 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1–6. IEEE (2020)
Chaudhary, Y., Mehta, M., Sharma, R., Gupta, D., Khanna, A., Rodrigues, J.J.: Efficient-CovidNet: deep learning based COVID-19 detection from chest x-ray images. In: 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM), pp. 1–6. IEEE (2021)
Aslan, M.F., Unlersen, M.F., Sabanci, K., Durdu, A.: CNN-based transfer learning-BiLSTM network: a novel approach for COVID-19 infection detection. Appl. Soft Comput. 98, 106912 (2021)
Sharma, V., Chhatwal, S., Singh, B., et al.: An explainable artificial intelligence based prospective framework for COVID-19 risk prediction. medRxiv (2021)
Ye, Q., Xia, J., Yang, G.: Explainable AI for COVID-19 CT classifiers: an initial comparison study. arXiv preprint arXiv:2104.14506 (2021)
Karim, M.R., Döhmen, T., Rebholz-Schuhmann, D., Decker, S., Cochez, M., Beyan, O.: DeepCOVIDExplainer: explainable COVID-19 diagnosis based on chest x-ray images. arXiv e-prints arXiv-2004 (2020)
Zhao, J., Zhang, Y., He, X., Xie, P.: COVID-CT-dataset: a CT scan dataset about COVID-19. arXiv preprint arXiv:2003.13865 (2020)
Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., Ghassemi, M.: COVID-19 image data collection: prospective predictions are the future. arXiv:2006.11988 (2020)
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Dipto, S.M., Afifa, I., Sagor, M.K., Reza, M., Alam, M.A. (2021). Interpretable COVID-19 Classification Leveraging Ensemble Neural Network and XAI. In: Rojas, I., Castillo-Secilla, D., Herrera, L.J., Pomares, H. (eds) Bioengineering and Biomedical Signal and Image Processing. BIOMESIP 2021. Lecture Notes in Computer Science(), vol 12940. Springer, Cham. https://doi.org/10.1007/978-3-030-88163-4_33
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DOI: https://doi.org/10.1007/978-3-030-88163-4_33
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