DETECTION OF COVID-19 IN LOW ENERGY CHEST X-RAYS USING FAST R-CNN

In recent years, it has been shown that deep learning can produce similar performance increases in the domain of medical image analysis for object detection and segmentation tasks. Notable recent work includes important medical applications, for example, in the field of pulmonology (classification of lung diseases and detection of pulmonary nodules on CT images in this paper, we present a variation of CNNs, which works extremely well on a current data set — a customized architecture with optimal parameters. In our contribution, we focus on lowering the complexity of our network, while yet reaching a phenomenally high degree of accuracy. To achieve this aim, our model has been tailored for high performance and an easy design.

___

  • Ai, T., Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, L. Xia, et. al. 2019. Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology, 2020, 296, 200642.
  • Alafif, T. 2020. Machine and deep learning towards COVID-19 diagnosis and treatment: survey, challenges, and future directions.
  • Albahri, O.S., A.A. Zaidan, A.S. Albahri, B.B. Zaidan, K.H. Abdulkareem, Z.T. Al-Qaysi, N.A. Rashid, et. al. 2020. Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of infection and public health 13, 1381–1396.
  • Butt, C.G., J. Chun, and. B.A. Babu. 2020. Deep learning system to screen coronavirus disease 2019 pneumonia. Applied Intelligence, pp 1
  • Das, N.N., N. Kumar, M. Kaur, V. Kumar, and D. Singh. 2020. Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays.
  • Dieterle, F.J. 2003. Multianalyte quantifications by means of integration of artificial neural networks, genetic algorithms and chemometrics for time-resolved analytical data.
  • El Asnaoui, K., and Y. Chawki. 2020. Using X-ray images and deep learning for automated detection of coronavirus disease. Journal of Biomolecular Structure and Dynamics,38, 1-12.
  • Fang, Y., H. Zhang, J. Xie, M. Lin, L. Ying, P. Pang, and W. Ji. 2020. Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 2020, 200432
  • Goodfellow, I., Y. Bengio, and A. Courville. 2017. Deep learning (adaptive computation and machine learning series). MIT Press: Cambridge, UK, 2016; p. 800.
  • Hammoudi, K., H. Benhabiles, M. Melkemi, F. Dornaika, I. Arganda-Carreras, D. Collard, and A. Scherpereel. 2020 Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19.
  • He, K., X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  • Huang, G., Z. Liu, L. Van Der Maaten, and K.Q. Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
  • Islam, M.Z., M.M. Islam, and A. Asraf. 2020. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Informatics in medicine unlocked, 20, 100412.
  • Karim, M., T. Döhmen, D. Rebholz-Schuhmann, S. Decker, M. Cochez, and O. Beyan. 2020. Deepcovidexplainer: Explainable covid-19 predictions based on chest x-ray images. arXiv preprint .arXiv:2004.04582
  • Khan, A.I., J.L. Shah, and M.M. Bhat. 2020. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine, 105581.
  • Krizhevsky, A., I. Sutskever, and G.E. Hinton. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
  • Luz, E., P.L. Silva, R. Silva, G. Moreira. 2020. Towards an efficient deep learning model for covid-19 patterns detection in x-ray images.
  • Maguolo, G., and L. Nanni. 2021. A critic evaluation of methods for covid-19 automatic detection from .x-ray images. arXiv:2004.12823
  • Qiu, J., J. Wang, S. Yao, K. Guo, B. Li, E. Zhou, H. Yang, et. al. 2016 Going deeper with embedded fpga platform for convolutional neural network. In Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (pp. 21-23).
  • Selvan, R., E. Dam, N.S. Detlefsen, S. Rischel, K. Sheng, M. Nielsen, and A. Pai. 2020. Lung segmentation from chest X-rays using variational data imputation.
  • Shi, F., J. Wang, J. Shi, Z. Wu, Q. Wang, Z. Tang, D. Shen, et. al. 2020. Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE reviews in biomedical engineering, 14, 4-15.
  • Shuja, J., E. Alanazi, W. Alasmary, and A. Alashaikh. 2020. COVID-19 open source data sets: a comprehensive survey. Applied Intelligence, 1-30
  • Simonyan, K., and A. Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In Proceedings of the International Conference on Learning Representations. Song, F., N. Shi, F. Shan, Z. Zhang, J. Shen, H. Lu, Y. Shi. 2020. Emerging 2019 novel coronavirus (2019-nCoV) pneumonia. Radiology, 295, 210-217.
  • Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, A. Rabinovich et. al. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition pp. 1-9.
  • Tan, M., and Q. Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning (pp. 6105-6114). PMLR.
  • Tartaglione, E., C.A. Barbano, C. Berzovini, M. Calandri, and M. Grangetto. 2020. Unveiling covid-19 from chest x-ray with deep learning: a hurdles race with small data. International Journal of Environmental Research and Public Health, 17(18), 6933.
  • Toğaçar, M., B. Ergen, and Z. Cömert. 2020. COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Computers in biology and medicine, 121, 103805.
  • Ucar, F., and D. Korkmaz. 2020. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based Diagnosis of the Coronavirus Disease 2019 (COVID-19) from X-ray images. Medical hypotheses, 140, 109761.
  • Vaid, S., R. Kalantar, and M. Bhandari. 2020. Deep learning COVID-19 detection bias: accuracy through artificial intelligence. International Orthopaedics, 44, 1539-1542.
  • Véstias, M.P. 2019. A survey of convolutional neural networks on edge with reconfigurable computing. Algorithms, 12(8), 154.
  • Victor, U., X. Dong, X. Li, P. Obiomon, and L. Qian. 2020. Effective covid-19 screening using chest radiography images via deep learning Training, vol. 7, pp. 152.
  • Waheed, A., M. Goyal, D. Gupta, A. Khanna, F. Al-Turjman, and P.R. Pinheiro. 2020. Covidgan: data augmentation using auxiliary classifier gan for improved covid-19 detection. Ieee Access, 8, 91916-91923
  • Zhang, L., and H. Schaeffer. 2020. Forward stability of ResNet and its variants. Journal of Mathematical Imaging and Vision, 62(3), 328-351.