A Narrative Review Of Machine Learning Systems In The Diagnosis Of CT Scan

Husain Zadeh, Zahra; Emamverdian, Fatemeh; Delazar, Sina

[Home ] [Archive]

International Journal of Medical Investigation

Main Menu

Home

Journal Information

Articles archive

For Authors

For Reviewers

Registration

Contact us

Site Facilities

Editorial Board

Search in website

Receive site information

Volume 10, Issue 2 (7-2021)

Int J Med Invest 2021, 10(2): 1-10

Back to browse issues page

A Narrative Review Of Machine Learning Systems In The Diagnosis Of CT Scan

Zahra Husain Zadeh

, Fatemeh Emamverdian ^*

, Sina Delazar

. Department of Health Information Technology, School of Management and Medical Information science, Tabriz University of Medical Sciences, Tabriz, Iran

Abstract: (2087 Views)

Clinicians rely on their knowledge and experience, as well as the results of complex and time-consuming clinical trials, despite the inevitable human error, to diagnose and treat diseases. The application of machine learning sheds light on the ability of these techniques to help correctly diagnose some diseases. Nowadays, with the emergence of the COVID-19 pandemic, the mass number of COVID-19 cases who refer to medical centers increased through the last two years of the pandemic, leading to a large number of patient records being collected in the medical database; while these resources not being used properly in some cases. In this regard, data mining and machine learning approaches have attracted researchers to help to predict and diagnosing disease. Through the literature, machine learning is applied to extract patients' characteristics and their correct classification to help diagnose suspicious cases. Due to the need for sufficient experience to diagnose the COVID-19 based on the imaging methods of the lung, researchers have thought of using intelligent diagnostic methods by modeling the knowledge of skilled radiologists using machine learning algorithms. Our study showed how machine learning-based analysis of lung imaging can lighten the burden on radiologists, as they must review and prioritize the growing number of imaging findings in a pandemic peak. Also, there is a need for big datasets for better training of Machine learning algorithms; while literature review shows the possibility of statistical errors like duplication of images in public imagining repositories of COVID-19. But yet it is unlikely that these machine learning algorithms will be used instead of standard nucleic acid tests as the primary tool for detecting COVID-19; while broad application of these inexpensive systems could occasion opportunistic screening of COVID-19 in lung scans.

Keywords: COVID-19, Imaging, CT Scan, Chest X-Ray, Machine Learning.

Full-Text [PDF 190 kb] (439 Downloads)

Type of Study: Review | Subject: General

References

1. 1. Ciotti M, Ciccozzi M, Terrinoni A, Jiang WC, Wang CB, Bernardini S. The COVID-19 pandemic. Critical reviews in clinical laboratory sciences. 2020 Aug 17;57(6):365-88. 2. Omer SB, Malani P, Del Rio C. The COVID-19 pandemic in the US: a clinical update. Jama. 2020 May 12;323(18):1767-8. 3. Chowell G, Mizumoto K. The COVID-19 pandemic in the USA: what might we expect?. The Lancet. 2020 Apr 4;395(10230):1093-4. 4. Zhai P, Ding Y, Wu X, Long J, Zhong Y, Li Y. The epidemiology, diagnosis and treatment of COVID-19. International journal of antimicrobial agents. 2020 May 1;55(5):105955. 5. Long C, Xu H, Shen Q, Zhang X, Fan B, Wang C, Zeng B, Li Z, Li X, Li H. Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT?. European journal of radiology. 2020 May 1;126:108961. 6. Pascarella G, Strumia A, Piliego C, Bruno F, Del Buono R, Costa F, Scarlata S, Agrò FE. COVID‐19 diagnosis and management: a comprehensive review. Journal of internal medicine. 2020 Aug;288(2):192-206. 7. Hafeez A, Ahmad S, Siddqui SA, Ahmad M, Mishra S. A review of COVID-19 (Coronavirus Disease-2019) diagnosis, treatments and prevention. EJMO. 2020;4(2):116-25. 8. Yüce M, Filiztekin E, Özkaya KG. COVID-19 diagnosis—A review of current methods. Biosensors and Bioelectronics. 2020 Oct 24:112752. 9. Litmanovich DE, Chung M, Kirkbride RR, Kicska G, Kanne JP. Review of chest radiograph findings of COVID-19 pneumonia and suggested reporting language. Journal of thoracic imaging. 2020 Nov 14;35(6):354-60. 10. Kashyape R, Jain R. The utility of HRCT in the initial diagnosis of COVID-19 pneumonia—An Indian perspective. The Indian Journal of Radiology & Imaging. 2021 Jan;31(Suppl 1):S178. 11. Cao Y, Han X, Gu J, Li Y, Liu J, Alwalid O, Cui Y, Zhang X, Zheng C, Fan Y, Wu H. Prognostic value of baseline clinical and HRCT findings in 101 patients with severe COVID-19 in Wuhan, China. Scientific reports. 2020 Oct 16;10(1):1-3. 12. Chen Z, Fan H, Cai J, Li Y, Wu B, Hou Y, Xu S, Zhou F, Liu Y, Xuan W, Hu H. High-resolution computed tomography manifestations of COVID-19 infections in patients of different ages. European journal of radiology. 2020 May 1;126:108972. 13. Alam SZ, Al Muid SM, Akhter A, Rahman AS, Al Emran M, Al Mostakim MT. HRCT Chest Evaluation of COVID-19 Patients: Experience in Combined Military Hospital Dhaka, Bangladesh. Journal of Bangladesh College of Physicians and Surgeons. 2020 Jun 9:21-8. 14. Yao X, Liu Y. Machine learning. InSearch Methodologies 2014 (pp. 477-517). Springer, Boston, MA. 15. Wagstaff K. Machine learning that matters. arXiv preprint arXiv:1206.4656. 2012 Jun 18. 16. Bi Q, Goodman KE, Kaminsky J, Lessler J. What is machine learning? A primer for the epidemiologist. American journal of epidemiology. 2019 Dec 31;188(12):2222-39. 17. Dietterich TG. Machine-learning research. AI magazine. 1997 Dec 15;18(4):97-. 18. Sra S, Nowozin S, Wright SJ, editors. Optimization for machine learning. Mit Press; 2012. 19. Zhang XD. Machine learning. InA Matrix Algebra Approach to Artificial Intelligence 2020 (pp. 223-440). Springer, Singapore. 20. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015 Jul 17;349(6245):255-60. 21. Decenciere E, Cazuguel G, Zhang X, Thibault G, Klein JC, Meyer F, Marcotegui B, Quellec G, Lamard M, Danno R, Elie D. TeleOphta: Machine learning and image processing methods for teleophthalmology. Irbm. 2013 Apr 1;34(2):196-203. 22. Narwaria M, Lin W. SVD-based quality metric for image and video using machine learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 2011 Sep 29;42(2):347-64. 23. Caggiano A, Zhang J, Alfieri V, Caiazzo F, Gao R, Teti R. Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP Annals. 2019 Jan 1;68(1):451-4. 24. Berg S, Kutra D, Kroeger T, Straehle CN, Kausler BX, Haubold C, Schiegg M, Ales J, Beier T, Rudy M, Eren K. Ilastik: interactive machine learning for (bio) image analysis. Nature Methods. 2019 Dec;16(12):1226-32. 25. Rymarczyk T, Kozłowski E, Kłosowski G, Niderla K. Logistic regression for machine learning in process tomography. Sensors. 2019 Jan;19(15):3400. 26. Zhang YC, Kagen AC. Machine learning interface for medical image analysis. Journal of digital imaging. 2017 Oct;30(5):615-21. 27. Kohli MD, Summers RM, Geis JR. Medical image data and datasets in the era of machine learning—whitepaper from the 2016 C-MIMI meeting dataset session. Journal of digital imaging. 2017 Aug;30(4):392-9. 28. Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. Ieee Access. 2017 Dec 29;6:9375-89. 29. Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: Overview, challenges and the future. Classification in BioApps. 2018:323-50. 30. Afshar P, Heidarian S, Enshaei N, Naderkhani F, Rafiee MJ, Oikonomou A, Fard FB, Samimi K, Plataniotis KN, Mohammadi A. COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning. Scientific Data. 2021 Apr 29;8(1):1-8. 31. Tsai EB, Simpson S, Lungren MP, Hershman M, Roshkovan L, Colak E, Erickson BJ, Shih G, Stein A, Kalpathy-Cramer J, Shen J. The RSNA International COVID-19 Open Radiology Database (RICORD). Radiology. 2021 Apr;299(1):E204-13. 32. Jiang Y, Chen H, Loew M, Ko H. Covid-19 ct image synthesis with a conditional generative adversarial network. IEEE Journal of Biomedical and Health Informatics. 2020 Dec 4;25(2):441-52. 33. Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X, Deng L, Zheng C, Zhou J, Shi H, Feng J. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nature communications. 2020 Oct 9;11(1):1-4. 34. Kumar R, Khan AA, Kumar J, Zakria A, Golilarz NA, Zhang S, Ting Y, Zheng C, Wang W. Blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging. IEEE Sensors Journal. 2021 Apr 30. 35. Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, Aviles-Rivero AI, Etmann C, McCague C, Beer L, Weir-McCall JR. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nature Machine Intelligence. 2021 Mar;3(3):199-217. 36. Ilyasova NY, Chesnokova TA. Development of the technique for automatic highlighting ranges of interest in lungs x-ray images. InCEUR Workshop Proceedings 2019 (pp. 128-133). 37. Patel PR, De Jesus O. CT Scan. StatPearls [Internet]. 2021 Jan 11. 38. Elaziz MA, Hosny KM, Salah A, Darwish MM, Lu S, Sahlol AT. New machine learning method for image-based diagnosis of COVID-19. Plos one. 2020 Jun 26;15(6):e0235187. 39. Kadry S, Rajinikanth V, Rho S, Raja NS, Rao VS, Thanaraj KP. Development of a machine-learning system to classify lung ct scan images into normal/covid-19 class. arXiv preprint arXiv:2004.13122. 2020 Apr 24. 40. Rish I. An empirical study of the naive Bayes classifier. InIJCAI 2001 workshop on empirical methods in artificial intelligence 2001 Aug 4 (Vol. 3, No. 22, pp. 41-46). 41. Hussain L, Nguyen T, Li H, Abbasi AA, Lone KJ, Zhao Z, Zaib M, Chen A, Duong TQ. Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection. BioMedical Engineering OnLine. 2020 Dec;19(1):1-8. 42. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, Cao K. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology. 2020 Mar 19. 43. 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. Scientific Reports. 2020 Nov 11;10(1):1-2. 44. Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine. 2020 Jun;43(2):635-40. 45. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, Bernheim A, Siegel E. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv preprint arXiv:2003.05037. 2020 Mar 10. 46. Hurt B, Yen A, Kligerman S, Hsiao A. Augmenting interpretation of chest radiographs with deep learning probability maps. Journal of thoracic imaging. 2020 Sep 1;35(5):285-93. 47. Alom MZ, Yakopcic C, Hasan M, Taha TM, Asari VK. Recurrent residual U-Net for medical image segmentation. Journal of Medical Imaging. 2019 Mar;6(1):014006. 48. Asif S, Wenhui Y, Jin H, Jinhai S. Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Network. In2020 IEEE 6th International Conference on Computer and Communications (ICCC) 2020 Dec 11 (pp. 426-433). IEEE. 49. Wang D, Mo J, Zhou G, Xu L, Liu Y. An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images. PloS one. 2020 Nov 17;15(11):e0242535. 50. Purkayastha S, Xiao Y, Jiao Z, Thepumnoeysuk R, Halsey K, Wu J, Tran TM, Hsieh B, Choi JW, Wang D, Vallières M. Machine learning-based prediction of COVID-19 severity and progression to critical illness using CT imaging and clinical data. Korean Journal of Radiology. 2021 Jan 5;22. 51. Yurttakal AH, Erbay H, İkizceli T, Karaçavuş S. Detection of breast cancer via deep convolution neural networks using MRI images. Multimedia Tools and Applications. 2020 Jun;79(21):15555-73. 52. Khatami A, Nazari A, Khosravi A, Lim CP, Nahavandi S. A weight perturbation-based regularisation technique for convolutional neural networks and the application in medical imaging. Expert systems with applications. 2020 Jul 1;149:113196. 53. Pratt H, Coenen F, Broadbent DM, Harding SP, Zheng Y. Convolutional neural networks for diabetic retinopathy. Procedia computer science. 2016 Jan 1;90:200-5. 54. Salehinejad H, Colak E, Dowdell T, Barfett J, Valaee S. Synthesizing chest x-ray pathology for training deep convolutional neural networks. IEEE transactions on medical imaging. 2018 Nov 14;38(5):1197-206. 55. Weisman AJ, Kieler MW, Perlman SB, Hutchings M, Jeraj R, Kostakoglu L, Bradshaw TJ. Convolutional neural networks for automated PET/CT detection of diseased lymph node burden in patients with lymphoma. Radiology: Artificial Intelligence. 2020 Sep 2;2(5):e200016. 56. Farhat H, Sakr GE, Kilany R. Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19. Machine vision and applications. 2020 Sep;31(6):1-42. 57. Wang T, Zhao Y, Zhu L, Liu G, Ma Z, Zheng J. Lung CT image aided detection COVID-19 based on Alexnet network. In2020 5th International Conference on Communication, Image and Signal Processing (CCISP) 2020 Nov 13 (pp. 199-203). IEEE. 58. Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Van Esesn BC, Awwal AA, Asari VK. The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164. 2018 Mar 3.

Add your comments about this article

‎ 20.1001.1.23222913.2021.10.2.19.5

Mendeley

Zotero

RefWorks

Husain Zadeh Z, Emamverdian F, Delazar S. A Narrative Review Of Machine Learning Systems In The Diagnosis Of CT Scan. Int J Med Invest 2021; 10 (2) :1-10
URL: http://intjmi.com/article-1-658-en.html

Rights and permissions
	This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Volume 10, Issue 2 (7-2021)

Back to browse issues page

Persian site map - English site map - Created in 0.05 seconds with 37 queries by YEKTAWEB 4645