Detection and Classification of Lung Diseases for Pneumonia and COVID-19 using Deep Learning Techniques
Keywords:
Computed Tomography, Machine Learning (ML) and Deep Learning (DL), Convolutional Neural Networks.Abstract
Rapid advancements in medical imaging technologies have increased the need for sophisticated diagnostic instruments that can reliably and quickly identify lung diseases. The primary goal of this research is to develop and apply deep learning or machine learning methods for the diagnosis and classification of COVID-19 and pneumonia, two dangerous respiratory infections. The research investigates the potential of artificial intelligence in analyzing medical imaging data, such as X-rays and CT scans, using state-of-the-art computational models and algorithms to distinguish between healthy and diseased lung tissues. Using large datasets with a variety of COVID-19 and pneumonia case examples, machine learning or deep learning models are trained using the selected methodology. These respiratory disorders can be accurately identified and classified thanks to the models' ability to learn complex patterns and features. The study also aims to explore the robustness and generalization capabilities of the models across different imaging modalities and populations. The results of the study offer a quick and non-invasive method for diagnosing lung conditions, which has important ramifications for medical diagnostics. The utilization of cutting-edge healthcare technologies has the potential to improve diagnostic precision, reduce staff workload, and enable timely interventions, especially when it comes to respiratory illnesses. The research's conclusions and insights reinforce current initiatives to use artificial intelligence to enhance healthcare outcomes and solve issues with respiratory disease diagnosis and treatment.
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Almomani, F. (2020) Kinetic Modeling of Microalgae Growth and CO2 Bio-Fixation Using Central Composite Design Statistical Approach. Science of the Total Environment, 720, Article ID: 137594. https://doi.org/10.1016/j.scitotenv.2020.137594.
Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study[J]. Lancet, 2020, 395(10223): 507−513. DOI :10.1016/S0140-6736(20)30211-7.
Cui J, Li F, Shi ZL. Origin and evolution of pathogenic coronaviruses[J]. Nat Rev Microbiol 2019, 17: 181-92.
Harjoseputro Y, Yuta I, Danukusumo K P. MobileNets: Efficient convolutional neural network for identification of protected birds[J]. International Journal on Advanced Science, Engineering and Information Technology, 2020, 10(6): 2 290-2 296. DOI: 10.18517/ijaseit. 10.6.10948.
Hu J, Shen L, Albanie S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2 011-2 023. DOI: 10.1109/TPAMI.2019.2913372.
Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China[J]. Lancet.2020; S0140-6736(20)30183-5. [Published online ahead of print; 2020 Jan 24]
Jiang X G, Ma C L, Du P, et al. A deep learning system to screen novel coronavirus disease 2019 pneumonia[J]. Engineering, 2020, 6(10): 1 122-1 129.
Khuntia, S., Sinha, M.K. and Saini, B. (2020) Conversion of NO2 through Ozonation and Peroxone Process in Gas and Aqueous Phase: Finding the Suitable Process through Experimental Route. Chemical Engineering Journal, 387, Article ID: 124082. https://doi.org/10.1016/j.cej.2020.124082.
Ling Shengli. The COVID-19 epidemic and non-traditional security governance [J]. Frontline, 2022, 7(1): 33-36. DOI: 10.3969/j.issn.0529-1445.2022. 07.010.
Liu Guangqian, Chen Dan, Ma Xingru. Design and application of smart environmental monitoring [J]. Electronic Journal, 2020(7): 007.
Nyirenda D, Ten HB W, Williams R, et al. Knowledge and practices of radiographers regarding infection control in radiology departments in Malawi[J]. Radiography 2019,
Shi HB, Han XY, Fan Y, et al. Radiologic features of patients with 2019-nCoV infection[J]. Journal of Clinical Radiology, 2020:1-8.
Sun L, Mo Z H, Yan F H, et al. Adaptive feature selection guided deep forest for COVID-19 classification with chest CT[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 24(10): 2 798-2 805. DOI: 10. 1109/JBHI.2020.3019505
Sun Yuanbo, Wen Zhiyi, Xu Ruige, Luo Fei, Li Ge. Review of novel coronavirus pneumonia epidemic data visualization design [J]. Packaging Engineering, 2020(8): 51-62.
Zhang Xueqin. Analysis of the effect of comprehensive nursing on postoperative pulmonary infection in patients with hypertensive cerebral hemorrhage[J]. Smart Health, 2020(8): 36-38.
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