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Exploring Markov Decision Processes: A Comprehensive Survey of Optimization Applications and Techniques
Qazi Waqas Khan
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Engineering Group Research Article 記事ID: igmin211

A Machine Learning-based Method for COVID-19 and Pneumonia Detection

Image Processing Machine LearningData Mining
Qazi Waqas Khan *
Affiliation

Affiliation

    Department of Computer Engineering, Jeju National University, Jejusi 63243, Jeju Special Self-Governing Province, Republic of Korea

DOI10.61927/igmin211 全文HTML 全文PDF この記事を引用する
DOI10.61927/igmin211 全文HTML 全文PDF この記事を引用する

要約

Pneumonia is described as an acute infection of lung tissue produced by one or more bacteria, and Coronavirus Disease (COVID-19) is a deadly virus that affects the lungs of the human body. The symptoms of COVID-19 disease are closely related to pneumonia. In this work, we identify the patients of pneumonia and coronavirus from chest X-ray images. We used a convolutional neural network for spatial feature learning from X-ray images. We experimented with pneumonia and coronavirus X-ray images in the Kaggle dataset. Pneumonia and corona patients are classified using a feed-forward neural network and hybrid models (CNN+SVM, CNN+RF, and CNN+Xgboost). The experimental findings on the Pneumonia dataset demonstrate that CNN detects Pneumonia patients with 99.47% recall. The overall experiments on COVID-19 x-ray images show that CNN detected the COVID-19 and pneumonia with 95.45% accuracy.

数字

Proposed Methodology Diagram. Figure 1: Proposed Methodology Diagram....
Pneumonia images. Figure 2: Pneumonia images....
Actual and predicted label images. Figure 3: Actual and predicted label images....

参考文献

    1. Aristanti S. The analysis of directive speech acts on World Health Organization's speech entitled "WHO Director General's opening remarks at the media briefing on COVID-19-11 May 2020". [dissertation]. UIN SMH Banten; 2021. Available from: https://repository.uinbanten.ac.id/6726/
    2. Birman D. Investigation of the effects of COVID-19 on different organs of the body. Eurasian J Chem Med Pet Res. 2023;2(1):24-36. Available from: https://www.ejcmpr.com/article_160994.html
    3. Tuncer T, Ozyurt F, Dogan S, Subasi A. A novel Covid-19 and pneumonia classification method based on F-transform. Chemometr Intell Lab Syst. 2021;210:104256. Available from: https://pubmed.ncbi.nlm.nih.gov/33531722/
    4. Field EL, Rodriguez AJ, Rajan M, Smith K. Efficacy of artificial intelligence in the categorisation of pediatric pneumonia on chest radiographs: a systematic review. Children (Basel). 2023;10(3):576. Available from: https://pubmed.ncbi.nlm.nih.gov/36980134/
    5. Irmici G, Cè M, Caloro E, Khenkina N, Della Pepa G, Ascenti V, et al. Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available? Diagnostics (Basel). 2023;13(2):216. Available from: https://pubmed.ncbi.nlm.nih.gov/36673027/
    6. Ukwuoma CC, Qin Z, Belal Bin Heyat M, Akhtar F, Bamisile O, Muaad AY, et al. A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images. J Adv Res. 2023;48:191-211. Available from: https://pubmed.ncbi.nlm.nih.gov/36084812/
    7. Ren H, Fengshi Jing, Zhurong Chen, Shan He, Jiandong Zhou, Le Liu, et al. CheXMed: a multimodal learning algorithm for pneumonia detection in the elderly. Inf Sci (Ny). 2024;654:119854. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0020025523014391
    8. Malik H, Anees T, Din M, Naeem A. CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays. Multimed Tools Appl. 2023;82(9):13855-13880. Available from: https://pubmed.ncbi.nlm.nih.gov/36157356/
    9. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology. 2020:200905. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233473/
    10. Zhang J, Xie Y, Pang G, Liao Z, Verjans J, Li W, Sun Z, He J, Li Y, Shen C, Xia Y. Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection. IEEE Trans Med Imaging. 2021;40(3):879-890. Available from: https://pubmed.ncbi.nlm.nih.gov/33245693/
    11. Ullah Z, Muhammad Usman, Siddique Latif, Jeonghwan Gwak. Densely attention mechanism based network for COVID-19 detection in chest X-rays. Sci Rep. 2023;13(1):261. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816547/
    12. Hemdan EE, Shouman MA, Karar ME. COVIDX-net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images. arXiv Preprint. 2020. Available from: https://arxiv.org/abs/2003.11055
    13. Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal Appl. 2021;24:1207-1220. Available from: https://pubmed.ncbi.nlm.nih.gov/33994847/
    14. Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis. 2020;20(4):425-434. Available from: https://www.thelancet.com/article/S1473-3099(20)30086-4/fulltext
    15. Toğaçar M, Ergen B, Cömert Z, Özyurt F. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. IRBM. 2020;41(4):212-222. Available from: https://www.sciencedirect.com/science/article/abs/pii/S1959031819301174
    16. Liang G, Zheng L. A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Comput Methods Programs Biomed. 2020;187:104964. Available from: https://pubmed.ncbi.nlm.nih.gov/31262537/
    17. Jaiswal AK, Tiwari P, Kumar S, Gupta D, Khanna A, Rodrigues JJ. Identifying pneumonia in chest X-rays: a deep learning approach. Measurement. 2019;145:511-518. Available from: https://uobrep.openrepository.com/handle/10547/623797
    18. Bandyopadhyay SK, Dutta S. Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release (preprint). 2020;172-177.
    19. Wang L, Lin ZQ, Wong A. COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep. 2020;10(1):19549. Available from: https://pubmed.ncbi.nlm.nih.gov/33177550/
    20. Sethy PK, Behera SK. Detection of coronavirus disease (COVID-19) based on deep features. Preprints. 2020; 2020030300. Available from: https://www.preprints.org/manuscript/202003.0300/v1
    21. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. 2020;121:103792. Available from: https://pubmed.ncbi.nlm.nih.gov/32568675/
    22. Apostolopoulos ID, Mpesiana TA. COVID-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020;43:635-640. Available from: https://pubmed.ncbi.nlm.nih.gov/32524445/
    23. Song Y, Zheng S, Li L, Zhang X, Zhang X, Huang Z, et al. Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE ACM Trans Comput Biol Bioinform. 2021;18(6):2775-2780. Available from: https://pubmed.ncbi.nlm.nih.gov/33705321/
    24. Bougourzi F, Fadi Dornaika, Cosimo Distante, Abdelmalik Taleb-Ahmed. Emb-trattunet: a novel edge loss function and transformer-CNN architecture for multi-classes pneumonia infection segmentation in low annotation regimes. Artif Intell Rev. 2024;57(4):90. Available from: https://hal.science/hal-04520691v1
    25. Guddanti SS, Apurva Padhye, Anil Prabhakar, Sridhar Tayur. Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM). Front Comput Sci. 2024;5:1286657. Available from: https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2023.1286657/full
    26. Wang Y, Liu ZL, Yang H, Li R, Liao SJ, Huang Y, et al. Prediction of viral pneumonia based on machine learning models analyzing pulmonary inflammation index scores. Comput Biol Med. 2024;169:107905. Available from: https://pubmed.ncbi.nlm.nih.gov/38159398/
    27. Li X, Xiong X, Liang Z, Tang Y. A machine learning diagnostic model for Pneumocystis jirovecii pneumonia in patients with severe pneumonia. Intern Emerg Med. 2023;18(6):1741-1749. Available from: https://pubmed.ncbi.nlm.nih.gov/37530943/

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