A Systematic Review and Analysis on Deep Learning Techniques Used in Diagnosis of Various Categories of Lung Diseases

Sreedevi Jasthy; Radhakrishna Vangipuram; Sushama Rani Dutta

doi:10.13164/mendel.2021.2.080

Sreedevi Jasthy, Mrs Research Scholar, Department of Computer Science and Engineering, KL University, INDIA
Radhakrishna Vangipuram, Dr Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India https://orcid.org/0000-0002-7373-6276
Sushama Rani Dutta, Dr Assistant Professor, Department of Computer Science and Engineering, KL University, INDIA

DOI: https://doi.org/10.13164/mendel.2021.2.080

Keywords: Lung Diseases, Deep Learning, Prediction, X-Ray, CT, Ultrasound

Abstract

One of the record killers in the world is lung disease. Lung disease denotes to many disorders affecting the lungs. These diseases can be identified through Chest X- Ray, Computed Tomography CT, Ultrasound tests. This study provides a systematic review on different types of Deep Learning (DL) designs, methods, techniques used by different researchers in diagnosing COVID-19, Pneumonia, Tuberculosis, Lung tumor, etc. In the present research study, a systematic review and analysis is carried by following PRISMA research methodology. For this study, more than 900 research articles are considered from various indexing sources such as Scopus and Web of Science. After several selection steps, finally a 40 quality research articles are included for detailed analysis. From this study, it is observed that majority of the research articles focused on DL techniques with Chest X-Ray images and few articles focused on CT scan images and very few have focused on Ultrasound images to identify the lung disease

References

Abbas, A., Abdelsamea, M. M., and Gaber, M. M. Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network. Applied Intelligence 51, 2 (2021), 854–864.

Aljawarneh, S., Radhakrishna, V., and Kumar, G. R. An imputation measure for data imputation and disease classification of medical datasets. In AIP Conference Proceedings (2019), vol. 2146, AIP Publishing LLC, p. 020001.

Alquran, H., Alsleti, M., Alsharif, R., Qasmieh, I. A., Alqudah, A. M., and Harun, N. H. B. Employing texture features of chest x-ray images and machine learning in covid-19 detection and classification. MENDEL Journal 27, 1 (2021), 9–17.

Apostolopoulos, I. D., and Mpesiana, T. A. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine 43, 2 (2020), 635–640.

Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., and Mohammadi, A. Application of deep learning technique to manage covid-19 in routine clinical practice using ct images: Results of 10 convolutional neural networks. Computers in biology and medicine 121 (2020), 103795.

Bandaru, R., Pola, S., Thadem, S. A., Pendyala, K., Vangipuram, R., and Vangipuram, S. K. Design and analysis of activation functions used in deep learning models. In The 7th International Conference on Engineering & MIS 2021 (2021), pp. 1–5.

Bardou, D., Zhang, K., and Ahmad, S. M. Lung sounds classification using convolutional neural networks. Artificial intelligence in medicine 88 (2018), 58–69.

Bhandary, A., Prabhu, G. A., Rajinikanth, V., Thanaraj, K. P., Satapathy, S. C., Robbins, D. E., Shasky, C., Zhang, Y.-D., Tavares, J. M. R., and Raja, N. S. M. Deeplearning framework to detect lung abnormality–a study with chest x-ray and lung ct scan images. Pattern Recognition Letters 129 (2020), 271–278.

Bishop, C. M., et al. Neural networks for pattern recognition. Oxford university press, 1995.

Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., Chen, Q., Huang, S., Yang, M., Yang, X., et al. Deep learningbased model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Scientific reports 10, 1 (2020), 1–11.

Chouhan, V., Singh, S. K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Damaˇseviˇcius, R., and De Albuquerque, V. H. C. A novel transfer learning based approach for pneumonia detection in chest x-ray images. Applied Sciences 10, 2 (2020), 559.

Chowdhury, M. E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., Islam, K. R., Khan, M. S., Iqbal, A., Al Emadi, N., et al. Can ai help in screening viral and covid-19 pneumonia? IEEE Access 8 (2020), 132665–132676.

Ciompi, F., Chung, K., Van Riel, S. J., Setio, A. A. A., Gerke, P. K., Jacobs, C., Scholten, E. T., Schaefer-Prokop, C., Wille, M. M., Marchiano, A., et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Scientific reports 7, 1 (2017), 1–11.

Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., and Shao, L. Infnet: Automatic covid-19 lung infection segmentation from ct images. IEEE Transactions on Medical Imaging 39, 8 (2020), 2626–2637.

Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., et al. Artificial intelligence for the detection of covid-19 pneumonia on chest ct using multinational datasets. Nature communications 11, 1 (2020), 1–7.

Hua, K.-L., Hsu, C.-H., Hidayati, S. C., Cheng, W.-H., and Chen, Y.-J. Computeraided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets and therapy 8 (2015).

Huang, L., Han, R., Ai, T., Yu, P., Kang, H., Tao, Q., and Xia, L. Serial quantitative chest ct assessment of covid-19: a deep learning approach. Radiology: Cardiothoracic Imaging 2, 2 (2020), e200075.

Khan, A. I., Shah, J. L., and Bhat, M. M. Coronet: A deep neural network for detection and diagnosis of covid-19 from chest x-ray images. Computer Methods and Programs in Biomedicine 196 (2020), 105581.

Lakhani, P., and Sundaram, B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284, 2 (2017), 574–582.

Lakshmanaprabu, S., Mohanty, S. N., Shankar, K., Arunkumar, N., and Ramirez, G. Optimal deep learning model for classification of lung cancer on ct images. Future Generation Computer Systems 92 (2019), 374–382.

Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., et al. Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct. Radiology (2020).

Masood, A., Sheng, B., Li, P., Hou, X., Wei, X., Qin, J., and Feng, D. Computer-assisted decision support system in pulmonary cancer detection and stage classification on ct images. Journal of biomedical informatics 79 (2018), 117–128.

Matousek, R., Dobrovsky, L., and Kudela, J. How to start a heuristic? utilizing lower bounds for solving the quadratic assignment problem. International Journal of Industrial Engineering Computations 13, 2 (2022), 151–164.

Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., and Soufi, G. J. Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning. Medical image analysis 65 (2020), 101794.

Nam, J. G., Park, S., Hwang, E. J., Lee, J. H., Jin, K.-N., Lim, K. Y., Vu, T. H., Sohn, J. H., Hwang, S., Goo, J. M., et al. Development and validation of deep learning– based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology 290, 1 (2019), 218–228.

Narin, A., Kaya, C., and Pamuk, Z. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Analysis and Applications (2021), 1–14.

Oh, Y., Park, S., and Ye, J. C. Deep learning covid-19 features on cxr using limited training data sets. IEEE transactions on medical imaging 39, 8 (2020), 2688–2700.

Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., and Acharya, U. R. Automated detection of covid-19 cases using deep neural networks with x-ray images. Computers in biology and medicine 121 (2020), 103792.

Pasa, F., Golkov, V., Pfeiffer, F., Cremers, D., and Pfeiffer, D. Efficient deep network architectures for fast chest x-ray tuberculosis screening and visualization. Scientific reports 9, 1 (2019), 1–9.

Pereira, R. M., Bertolini, D., Teixeira, L. O., Silla Jr, C. N., and Costa, Y. M. Covid-19 identification in chest x-ray images on flat and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine 194 (2020), 105532.

Roy, S., Menapace, W., Oei, S., Luijten, B., Fini, E., Saltori, C., Huijben, I., Chennakeshava, N., Mento, F., Sentelli, A., et al. Deep learning for classification and localization of covid-19 markers in point-of-care lung ultrasound. IEEE transactions on medical imaging 39, 8 (2020), 2676–2687.

Shen, S., Han, S. X., Aberle, D. R., Bui, A. A., and Hsu, W. An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification. Expert systems with applications 128 (2019), 84–95.

Shen, W., Zhou, M., Yang, F., Yu, D., Dong, D., Yang, C., Zang, Y., and Tian, J. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognition 61 (2017), 663–673.

Singh, D., Kumar, V., Kaur, M., et al. Classification of covid-19 patients from chest ct images using multi-objective differential evolution–basedconvolutional neural networks. European Journal of Clinical Microbiology & Infectious Diseases 39, 7 (2020), 1379–1389.

Song, Q., Zhao, L., Luo, X., and Dou, X. Using deep learning for classification of lung nodules on computed tomography images. Journal of healthcare engineering 2017 (2017).

Stephen, O., Sain, M., Maduh, U. J., and Jeong, D.-U. An efficient deep learning approach to pneumonia classification in healthcare. Journal of healthcare engineering 2019 (2019).

Togac¸ar, M., Ergen, B., and C¨omert, Z. 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 (2020), 103805.

Vangipuram, S. k., and Appusamy, R. Machine learning framework for covid-19 diagnosis. In International Conference on Data Science, Elearning and Information Systems 2021 (2021), pp. 18–25.

Vangipuram, S. k., and Appusamy, R. A survey on similarity measures and machine learning algorithms for classification and prediction. In International Conference on Data Science, Elearning and Information Systems 2021 (2021), pp. 198–204.

Waheed, A., Goyal, M., Gupta, D., Khanna, A., Al-Turjman, F., and Pinheiro, P. R. Covidgan: data augmentation using auxiliary classifier gan for improved covid-19 detection. Ieee Access 8 (2020), 91916–91923.

Wang, C., Elazab, A., Wu, J., and Hu, Q. Lung nodule classification using deep feature fusion in chest radiography. Computerized Medical Imaging and Graphics 57 (2017), 10–18.

Wang, L., Lin, Z. Q., and Wong, A. Covidnet: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports 10, 1 (2020), 1–12.

Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., et al. A deep learning algorithm using ct images to screen for corona virus disease (covid-19). European radiology (2021), 1–9.

Wang, S., Zha, Y., Li, W., Wu, Q., Li, X., Niu, M., Wang, M., Qiu, X., Li, H., Yu, H., et al. A fully automatic deep learning system for covid-19 diagnostic and prognostic analysis. European Respiratory Journal 56, 2 (2020).

Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., and Zheng, C. A weaklysupervised framework for covid-19 classification and lesion localization from chest ct. IEEE transactions on medical imaging 39, 8 (2020), 2615–2625.

Xie, H., Yang, D., Sun, N., Chen, Z., and Zhang, Y. Automated pulmonary nodule detection in ct images using deep convolutional neural networks. Pattern Recognition 85 (2019), 109–119.

Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., et al. A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering 6, 10 (2020), 1122–1129.

Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., et al. Clinically applicable ai system for accurate diagnosis, quantitative measurements, and prognosis of covid-19 pneumonia using computed tomography. Cell 181, 6 (2020), 1423–1433.