Journal Press India^®

Author Details ( * ) denotes Corresponding author

Lung diseases are increasing day by day when compared to other diseases. The world has been experiencing COVID-19 since 2020 and one has no answer how to cure it in the initial days. Near future majority of the humanity will suffer from lung diseases based on studies. In this context, we are exploring contemporary artificial techniques (AI) and explaining how these will help in detecting the disease. Among Convolutional neural nets (CNNs), Regional Convolutional neural nets (RCNNs), and Vision Transformers(VT) state-of-the-art methods. The techniques are very diversified from the concept point of view. Each carries out the process in an expert wise. One who wants to automate the process can make an ensemble of these models.

Keywords

Convolutional neural nets; Vision transformers; Regional Convolutional neural nets, Lung disease

1. A. Narin, C. Kaya, Z. Pamuk, Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks, Pattern Analysis and Applications 24 (3) (2021) 1207–1220. doi:10.1007/s10044-021-00984-y. URL https://doi.org/10.1007/s10044-021-00984-y


2. H. Mukherjee, S. Ghosh, A. Dhar, S. M. Obaidullah, K. C. Santosh, K. Roy, Shallow convolutional neural network for covid-19 outbreak screening using chest x-rays, Cognitive Computation (Feb 2021). doi:10.1007/s12559-020-09775-9. URL https://doi.org/10.1007/s12559-020-09775-9


3. K. Sahinbas, F. O. Catak, 24 - transfer learning-based convolutional neural network for covid-19 detection with x-ray images, in: U. Kose, D. Gupta, V. H. C. de Albuquerque, A. Khanna (Eds.), Data Science for COVID-19, Academic Press, 2021, pp. 451–466. doi:https://doi.org/10.1016/B978-0-12-824536-1.00003-4. URL https://www.sciencedirect.com/science/article/pii/B9780128245361000034


4. S. Podder, S. Bhattacharjee, A. Roy, An efficient method of detection of covid-19 using mask r-cnn on chest x-ray images, AIMS Biophys 8 (3) (2021) 281–290.


5. S. Yao, Y. Chen, X. Tian, R. Jiang, Pneumonia detection using an improved algorithm based on faster r-cnn, Computational and Mathematical Methods in Medicine 2021 (2021) 8854892. doi:10.1155/2021/8854892. URL https://doi.org/10.1155/2021/8854892


6. A. Kundu, C. Mishra, S. Bilgaiyan, Covid-segnet: Diagnosis of covid-19 cases on radiological images using mask r-cnn, in: 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), 2021, pp. 1–5. doi:10.1109/ICBSII51839.2021.9445190.


7. D. Shome, T. Kar, S. N. Mohanty, P. Tiwari, K. Muhammad, A. Al- Tameem, Y. Zhang, A. K. J. Saudagar, Covid-transformer: Interpretable covid-19 detection using vision transformer for healthcare, International Journal of Environmental Research and Public Health 18 (21) (2021). doi:10.3390/ijerph182111086. URL https://www.mdpi.com/1660-4601/18/21/11086


8. P. Monkam, S. Qi, H. Ma, W. Gao, Y. Yao, W. Qian, Detection and classification of pulmonary nodules using convolutional neural networks: A survey, IEEE Access PP (2019) 1–1. doi:10.1109/ACCESS.2019.2920980.


9. I. Ahmed, A. Ahmad, G. Jeon, An iot-based deep learning framework for early assessment of covid-19, IEEE Internet of Things Journal 8 (21) (2021) 15855–15862. doi:10.1109/JIOT.2020.3034074.


10. R. C. Joshi, S. Yadav, V. K. Pathak, H. S. Malhotra, H. V. S. Khokhar, A. Parihar, N. Kohli, D. Himanshu, R. K. Garg, M. L. B. Bhatt, R. Kumar, N. P. Singh, V. Sardana, R. Burget, C. Alippi, C. M. Travieso-Gonzalez, M. K. Dutta, A deep learning-based covid-19 automatic diagnostic framework using chest x-ray images, Biocybernetics and Biomedical Engineering 41 (1) (2021) 239–254. doi:https://doi.org/10.1016/j.bbe.2021.01.002. URL https://www.sciencedirect.com/science/article/pii/S0208521621000036


11. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale (2021). arXiv:2010.11929.


12. K. S. Krishnan, K. S. Krishnan, Vision transformer based covid-19 detection using chest x-rays, in: 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), 2021, pp. 644–648. doi:10.1109/ISPCC53510.2021.9609375.


13. K. Tyagi, G. Pathak, R. Nijhawan, A. Mittal, Detecting pneumonia using vision transformer and comparing with other techniques, in: 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2021, pp. 12–16. doi:10.1109/ICECA52323.2021.9676146.


14. L. T. Duong, N. H. Le, T. B. Tran, V. M. Ngo, P. T. Nguyen, Detection of tuberculosis from chest x-ray images: Boosting the performance with vision transformer and transfer learning, Expert Systems with Applications 184 (2021) 115519. doi:https://doi.org/10.1016/j.eswa.2021.115519. URL https://www.sciencedirect.com/science/article/pii/S0957417421009295


15. S. Ren, K. He, R. Girshick, J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks (2016). arXiv:1506.01497.


16. K. Long, L. Tang, X. Pu, Y. Ren, M. Zheng, L. Gao, C. Song, S. Han, M. Zhou, F. Deng, Probability-based mask r-cnn for pulmonary embolism detection, Neurocomputing 422 (2021) 345–353. doi:https://doi.org/10.1016/j.neucom.2020.10.022. URL https://www.sciencedirect.com/science/article/pii/S0925231220315186


17. L. J. Muhammad, J. M. Amshi, S. S. Usman, I. A. Badi, I. Mohammed, O. S. Dada, A. A. Haruna, Deep learning models for classification and localization of covid-19 abnormalities on chest radiographs, in: 2021 4^th International Conference on Computing Information Sciences (ICCIS), 2021, pp. 1–6. doi:10.1109/ICCIS54243.2021.9676401.


18. S. R. Jena, S. T. George, D. N. Ponraj, Lung cancer detection and classification with dgmm-rbcnn technique, Neural Computing and Applications 33 (22) (2021) 15601–15617. doi:10.1007/s00521-021-06182-5. URL https://doi.org/10.1007/s00521-021-06182-5


19. D. Saikrishna, M. M. Rao, B. S. Dhanush, S. Harshavardhan, B. Prudhvi, P. Rana, U. Mittal, Pneumonia detection using deep learning algorithms, in: 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), 2021, pp. 282–287. doi:10.1109/ICIEM51511.2021.9445310.


20. E. E.-D. Hemdan, M. A. Shouman, M. E. Karar, Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images (2020). URL http://arxiv.org/abs/2003.11055



 
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