2023, Volume 7 Issue 2
Pneumonia Detection Using Image Enhancing Techniques and Deep Learning
AUTHOR(S)
Varshini S, Ramprasad R, Sivakumar M
DOI: https://doi.org/10.46647/ijetms.2023.v07i02.082
ABSTRACT
Pneumonia is a lung inflammation that mostly affects the tiny air sacs known as alveoli. The disorder can range in severity. The most prevalent causes of pneumonia are infections with viruses or bacteria, other microbes, or certain drugs. Cystic fibrosis, chronic obstructive pulmonary disease (COPD), asthma, diabetes, heart failure, a history of smoking, having a defective cough reflex, such as after a stroke, and having a weakened immune system are risk factors. The physical exam and symptoms are frequently used to make a diagnosis. One of the most common illnesses that are challenging to diagnose because of a shortage of professionals is pneumonia. Early and accurate diagnosis is crucial for effective treatment and better patient outcomes. Pneumonia, along with Covid-19, became one of the more serious medical conditions. The most popular procedure for diagnosis is a chest X-ray. In recent years, deep learning-based approaches have shown great promise in automated pneumonia detection using chest X-ray images. However, examining a chest X-ray is a difficult task. It follows that automated diagnostic systems are necessary. Hence one such system is the proposed CNN model described in this paper with an accuracy of 97.02%. It comprises of image enhancing techniques specially designed for X-ray images and the proposed CNN model.
Page No: 762 - 771
References:
[1] A. Tilve, S. Nayak, S. Vernekar, D. Turi, P. R. Shetgaonkar and S. Aswale, "Pneumonia Detection Using Deep Learning Approaches," 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India, 2020, pp. 1-8, doi: 10.1109/ic-ETITE47903.2020.152.
[2] Puneet Gupta, “Pneumonia Detection Using Convolutional Neural Networks”, International Journal for Modern Trends in Science and Technology, Vol. 07, Issue 01, January 2021, pp.- 77-80, doi: 10.46501/IJMTST070117.
[3] D. Srivastav, A. Bajpai, and P. Srivastava, "Improved Classification for Pneumonia Detection using Transfer Learning with GAN based Synthetic Image Augmentation," 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2021, pp. 433-437, doi: 10.1109/Confluence51648.2021.9377062.
[4] arXiv:1711.05225 [cs.CV] https://doi.org/10.48550/arXiv.1711.05225
[5] Hammoudi, K., Benhabiles, H., Melkemi, M. et al. Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19. J Med Syst 45, 75 (2021). https://doi.org/10.1007/s10916-021-01745-4
[6] Zhang, X., Lu, S., Wang, SH. et al. Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture. J. Comput. Sci. Technol. 37, 330–343 (2022). https://doi.org/10.1007/s11390-020-0679-8
[7] Wasif Khan;Nazar Zaki;Luqman Ali; (2021). Intelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review . IEEE Access, (), –. doi:10.1109/access.2021.3069937
[8] Hao Ren;Aslan B. Wong;Wanmin Lian;Weibin Cheng;Ying Zhang;Jianwei He;Qingfeng Liu;Jiasheng Yang;Chen Jason Zhang;Kaishun Wu;Haodi Zhang; (2021). Interpretable Pneumonia Detection by Combining Deep Learning and Explainable Models With Multisource Data . IEEE Access, (), –. doi:10.1109/ACCESS.2021.3090215
[9] S. Naidu, A. Quadros, A. Natekar, P. Parvatkar, K. M. Chaman Kumar and S. Aswale, "Enhancement of X-ray images using various Image Processing Approaches," 2021 International Conference on Technological Advancements and Innovations (ICTAI), Tashkent, Uzbekistan, 2021, pp. 115-120, doi: 10.1109/ICTAI53825.2021.9673317.
[10] Ikhsan, Ili & Hussain, Aini & Zulkifley, Mohd & Tahir, Noorita & Mustapha, Aouache. (2014). An analysis of x-ray image enhancement methods for vertebral bone segmentation. Proceedings - 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, CSPA 2014. 208-211. 10.1109/CSPA.2014.6805749.
[11] L. Wang and A. Wong, “Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest radiography images,” 2020. https://arxiv.org/abs/2003.09871v1
[12] J. P. Cohen, P. Morrison, and L. Dao, “Covid-19 image data collection,” 2020. https://arxiv.org/abs/2003.11597
[13] A. Wong, M. J. Shafiee, B. Chwyl, and F. Li, “Ferminets: Learning generative machines to generate efficient neural networks via generative synthesis,” CoRR, vol. abs/1809.05989, 2018. http: //arxiv.org/abs/1809.05989
[14] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. https://doi.org/10.1109/CVPR.2016.90
[15] P. Hunter, “The spread of the covid-19 coronavirus,” EMBO reports,
vol. n/a, no. n/a, p. e50334. https://doi.org/10.15252/embr.202050334
[16] “Covid-19: What we know so far about the 2019 novel coronavirus,” March 2020. https://www.uchicagomedicine.org/forefront/prevention-and- screening- articles/wuhan- coronavirus
[17] “Treatment for coronavirus disease (covid-19),” March 2020. https: //www.healthline.com/health/coronavirus- treatment#available- treatment
[18] D.Kermany,M.Goldbaum,W.Cai,C.Valentim,H.-Y.Liang,S.Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell, vol. 172, pp. 1122–1131.e9, 02 2018. https://doi.org/10.1016/j.cell.2018.02.010
How to Cite This Article:
Varshini S, Ramprasad R, Sivakumar M
. Pneumonia Detection Using Image Enhancing Techniques and Deep Learning
. ijetms;7(2):762-771. DOI: 10.46647/ijetms.2023.v07i02.082