Effective Survey on Detection and Classification of COVID-19 Suspected Individual Using CT scan Images

Authors

  • Snehal R. Sambhe  Department of Computer Science and Engineering, Government College of Engineering, Amravati, Maharashtra, India
  • Dr. Kamlesh A. Waghmare  Assistant Professor, Department of Computer Science and Engineering, Government College of Engineering, Amravati, Maharashtra, India

DOI:

https://doi.org/10.32628/CSEIT217339

Keywords:

COVID 19, Classification, Corona virus, Chest Computed Tomography images

Abstract

As insufficient testing kits are available, the development of new testing kits for detecting COVID remains an open vicinity of research. It’s impossible to test each and every patient suffering from coronavirus symptoms using the traditional method i.e. RT-PCR. This test requires more time to produce results and have less sensitivity. Detecting feasible coronavirus infection using chest X-Ray may also assist quarantine excessive risk sufferers while testing results are disclosed. A learning model can be built based on CT scan images or Chest X-rays of individuals with higher accuracy. This paper represents a computer-aided diagnosis of COVID 19 infection bases on a feature extractor by using CNN models.

References

  1. Kabid Hassan Shibly, Samrat Kumar Dey, Md Tahzib-Ul Islam, Md Mahbubur Rahman, COVID faster R–CNN: A novel framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray images, Informatics in Medicine Unlocked, Volume 20, 2020, 100405, ISSN 2352-9148, https://doi.org/10.1016/j.imu.2020.100405.
  2. Ezz El-Din Hemdan and Marwa A. Shouman and Mohamed Esmail Karar. COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images. 2020, eprint:2003.11055.
  3. Kaur, M., Singh, D. Fusion of medical images using deep belief networks. Cluster Comput 23, 1439–1453 (2020). https://doi.org/10.1007/s10586-019-02999-x.
  4. Kumar Shukla P, Kumar Shukla P, Sharma P, Rawat P, Samar J, Moriwal R, Kaur M. Efficient prediction of drug-drug interaction using deep learning models. IET Syst Biol. 2020 Aug;14(4):211-216. doi: 10.1049/iet-syb.2019.0116. PMID: 32737279.
  5. Manjit Kaur,Hemant Kumar Gianey, Dilbag Singh, Munish Sabharwal. Multi-objective differential evolution based random forest for e-health applications, Journal Article 2019, Modern Physics Letters B, 1950022, 33(05), doi1: 0.1142/S0217984919500222
  6. Yu Y., Lin H., Meng J., Wei X., Guo H., Zhao Z.; Deep Transfer Learning for Modality Classification of Medical Images. Information 2017, 8, 91. https://doi.org/10.3390/info8030091
  7. Nardelli, P., Jimenez-Carretero, D., Bermejo-Pelaez, D., Washko, G. R., Rahaghi, F. N., Ledesma-Carbayo, M. J., & San Jose Estepar, R. (2018). Pulmonary Artery-Vein Classification in CT Images Using Deep Learning. IEEE transactions on medical imaging, 37(11), 2428–2440. https://doi.org/10.1109/TMI.2018.2833385
  8. Shin, H. C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016). Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE transactions on medical imaging, 35(5), 1285–1298. https://doi.org/10.1109/TMI.2016.2528162
  9. Xie, Y., Xia, Y., Zhang, J., Song, Y., Feng, D., Fulham, M., & Cai, W. (2019). Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT. IEEE transactions on medical imaging, 38(4), 991–1004. https://doi.org/10.1109/TMI.2018.2876510
  10. Hagerty, J. R., Stanley, R. J., Almubarak, H. A., Lama, N., Kasmi, R., Guo, P., Drugge, R. J., Rabinovitz, H. S., Oliviero, M., & Stoecker, W. V. (2019). Deep Learning and Handcrafted Method Fusion: Higher Diagnostic Accuracy for Melanoma Dermoscopy Images. IEEE journal of biomedical and health informatics, 23(4), 1385–1391. https://doi.org/10.1109/JBHI.2019.2891049
  11. Gerard, S. E., Patton, T. J., Christensen, G. E., Bayouth, J. E., & Reinhardt, J. M. (2019). FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images. IEEE transactions on medical imaging, 38(1), 156–166. https://doi.org/10.1109/TMI.2018.2858202
  12. Setio, A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S. J., Wille, M. M., Naqibullah, M., Sanchez, C. I., & van Ginneken, B. (2016). Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks. IEEE transactions on medical imaging, 35(5), 1160–1169. https://doi.org/10.1109/TMI.2016.2536809
  13. XIA, Kaijian & YIN, Hongsheng & Qian, Pengjiang & Jiang, Yizhang & Wang, Shuihua. (2019). Liver Semantic Segmentation Algorithm Based on Improved Deep Adversarial Networks in Combination of Weighted Loss Function on Abdominal CT Images. IEEE Access. PP. 1-1. 10.1109/ACCESS.2019.2929270.
  14. Zreik, M., van Hamersvelt, R. W., Wolterink, J. M., Leiner, T., Viergever, M. A., & Isgum, I. (2019). A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography. IEEE transactions on medical imaging, 38(7), 1588–1598. https://doi.org/10.1109/TMI.2018.2883807
  15. Bhandary, Abhir & Prabhu, Ananth & Rajinikanth, Venkatesan & Krishnan, Palani & Satapathy, Suresh & Robbins, David & Shasky, Charles & Zhang, Yu-Dong & Tavares, Joao & Raja, N. (2020). Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images. Pattern Recognition Letters. 129. 271-278. 10.1016/j.patrec.2019.11.013.
  16. Xiaohong W. Gao, Carl James-Reynolds, Edward Currie. Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture, Neurocomputing, Volume 392, 2020, Pages 233-244, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2018.12.086.
  17. Pannu, H.S., Singh, D. and Malhi, A.K. (2018), Improved Particle Swarm Optimization Based Adaptive Neuro‐Fuzzy Inference System for Benzene Detection. Clean – Soil, Air, Water, 46: 1700162. https://doi.org/10.1002/clen.201700162.
  18. Pannu, H.S., Singh, D. & Malhi, A.K. Multi-objective particle swarm optimization-based adaptive neuro-fuzzy inference system for benzene monitoring. Neural Comput & Applic 31, 2195–2205 (2019). https://doi.org/10.1007/s00521-017-3181-7
  19. Xingyu Zeng, Wanli Ouyang, Bin Yang, Junjie Yan, Xiaogang Wang. Gated Bi-directional CNN for Object Detection. 2016, 14th European Conference on Computer Vision, ECCV.
  20. Saha, Sujay & Khabir, Kanij & Abir, Shadman & Islam, Ariful. (2019). A newly proposed object detection method using Faster R-CNN inception with ResNet based on Tensorflow. 34. 10.1117/12.2523930.
  21. Nabin K. Mishra and M. Emre Celebi. An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning, 1601.07843, 2016.
  22. Pathan, Sameena & P C, Siddalingaswamy & Prabhu, K. (2017). Techniques and algorithms for computer aided diagnosis of pigmented skin lesions—A review. Biomedical Signal Processing and Control. 39. 237-262. 10.1016/j.bspc.2017.07.010.
  23. You, J., McLeod, R. D., & Hu, P. (2019). Predicting drug-target interaction network using deep learning model. Computational biology and chemistry, 80, 90–101.https://doi.org/10.1016/j.compbiolchem.2019.03.016
  24. Shih-Chung B. Lo, Heang-Ping Chan, Jyh-Shyan Lin, Huai Li, Matthew T. Freedman, Seong K. Mun. Artificial convolution neural network for medical image pattern recognition, Neural Networks, Volume 8, Issues 7–8, 1995, Pages 1201-1214, ISSN 0893-6080, https://doi.org/10.1016/0893-6080(95)00061-5.
  25. Yue, K., Zou, B., Wang, L., Li, X., Zeng, M. and Wei, F. (2017), Prediction of Drug‐Drug Interactions Based on Multi‐layer Feature Selection and Data Balance. Chinese J. Electron., 26: 585-590. https://doi.org/10.1049/cje.2017.04.005

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-06-30

Issue

Volume 7, Issue 3, May-June-2021

Section

Research Articles

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How to Cite

[1]
Snehal R. Sambhe, Dr. Kamlesh A. Waghmare, " Effective Survey on Detection and Classification of COVID-19 Suspected Individual Using CT scan Images" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.294-299, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT217339