Detection of COVID-19 Lung Lesions in Computed Tomography Images Using Deep Learning

Authors

DOI:

https://doi.org/10.17488/RMIB.43.1.1

Keywords:

COVID-19, Lung Lesions, Classification, Deep Learning, Computed Tomography

Abstract

The novel coronavirus (COVID-19) is a disease that mainly affects the lung tissue. The detection of lesions caused by this disease can help to provide an adequate treatment and monitoring its evolution. This research focuses on the binary classification of lung lesions caused by COVID-19 in images of computed tomography (CT) using deep learning. The database used in the experiments comes from two independent repositories, which contains tomographic scans of patients with a positive diagnosis of COVID-19. The output layers of four pre-trained convolutional networks were adapted to the proposed task and re-trained using the fine-tuning technique. The models were validated with test images from the two database’s repositories. The model VGG19, considering one of the repositories, showed the best performance with 88% and 90.2% of accuracy and recall, respectively. The model combination using the soft voting technique presented the highest accuracy (84.4%), with a recall of 94.4% employing the data from the other repository. The area under the receiver operating characteristic curve was 0.92 at best. The proposed method based on deep learning represents a valuable tool to automatically classify COVID-19 lesions on CT images and could also be used to assess the extent of lung infection.

Downloads

Download data is not yet available.

References

Rothan H, Byrareddy S. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J Autoimmun [Internet]. 2020;109:102433. Available from: https://doi.org/10.1016/j.jaut.2020.102433

Palacios Cruz M, Santos E, Velázquez Cervantes MA, León Juárez M. COVID-19, a worldwide public health emergency. Rev Clin Esp [Internet]. 2021;221(1):55–61. Available from: https://doi.org/10.1016/j.rce.2020.03.001

World Health Organization. Coronavirus Disease (COVID-19) Dashboard [Internet]. WHO Coronavirus Disease (COVID-19) Dashboard; 2021. Available from: https://covid19.who.int/

Instituto Mexicano del Seguro Social. Algoritmos interinos para la atención del COVID-19. Gobierno de México [Internet]. 2020; 1–31. Available from: http://educacionensalud.imss.gob.mx/es/system/files/Algoritmos_interinos_COVID19_CTEC.pdf

He JL, Luo L, Luo ZD, Lyu JX, et al. Diagnostic performance between CT and initial real-time RT-PCR for clinically suspected 2019 coronavirus disease (COVID-19) patients outside Wuhan, China. Respir Med [Internet]. 2020;168:105980. Available from: https://doi.org/10.1016/j.rmed.2020.105980

Ai T, Yang Z, Hou H, Zhan C, et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology [Internet]. 2020;296(2):E32–40. Available from: https://doi.org/10.1148/radiol.2020200642

Rubin GD, Ryerson CJ, Haramati LB, Sverzellati N, et al. The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic: A Multinational Consensus Statement From the Fleischner Society. Radiology [Internet]. 2020;296(1):172-180. Available from: https://doi.org/10.1148/radiol.2020201365

Shen M, Zhou Y, Ye J, AL-maskri AAA, et al. Recent advances and perspectives of nucleic acid detection for coronavirus. J Pharm Anal [Internet]. 2020;10(2):97–101. Available from: https://doi.org/10.1016/j.jpha.2020.02.010

Araujo Oliveira B, Campos de Oliveira L, Cerdeira Sabino E, Okay TS. SARS-CoV-2 and the COVID-19 disease: A mini review on diagnostic methods. Rev Inst Med Trop Sao Paulo [Internet]. 2020;62:e44. Available from: https://doi.org/10.1590/S1678-9946202062044

Uysal E, Kilinçer A, Cebeci H, Özer H, et al. Chest CT findings in RT-PCR positive asymptomatic COVID-19 patients. Clin Imaging [Internet]. 2021;77:37–42. Available from: https://doi.org/10.1016/j.clinimag.2021.01.030

Tahamtan A, Ardebili A. Real-time RT-PCR in COVID-19 detection: issues affecting the results. Expert Rev Mol Diagn [Internet]. 2020;20(5):453–4. Available from: https://doi.org/10.1080/14737159.2020.1757437

Li X, Zeng W, Li X, Chen H, et al. CT imaging changes of corona virus disease 2019(COVID-19): A multi-center study in Southwest China. J Transl Med [Internet]. 2020;18:154. Available from: https://doi.org/10.1186/s12967-020-02324-w

Yang X, He X, Zhao J, Zhang Y, et al. COVID-CT-Dataset: A CT Scan Dataset about COVID-19. arXiv:2003.13865 [Preprint]. 2020. Available from: https://arxiv.org/abs/2003.13865

Bernheim A, Mei X, Huang M, Yang Y, et al. Chest CT findings in coronavirus disease 2019 (COVID-19): Relationship to duration of infection. Radiology [Internet]. 2020;295(3):685–91. Available from: https://doi.org/10.1148/radiol.2020200463

Naudé W. Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. AI Soc [Internet]. 2020;35(3):761–765. Available from: https://doi.org/10.1007/s00146-020-00978-0

Bullock J, Luccioni A, Pham KH, Lam C, et al. Mapping the Landscape of Artificial Intelligence Applications against COVID-19. arXiv:2003.11336 [Preprint]. 2020;1–32. Available from: http://arxiv.org/abs/2003.11336

Li L, Qin L, Xu Z, Yin Y, et al. Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology [Internet]. 2020;296(2):E65–71. Available from: https://doi.org/10.1148/radiol.2020200905

Shah V, Keniya R, Shridharani A, Punjabi M, et al. Diagnosis of COVID-19 using CT scan images and deep learning techniques. Emerg Radiol [Internet]. 2021;28: 497–505. Available from: https://doi.org/10.1007/s10140-020-01886-y

Ma J, Wang Y, An X, Ge C, et al. Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation. Med Phys [Internet]. 2021;48(3):1197-1210. Available from: https://doi.org/10.1002/mp.14676

Jun M, Cheng G, Yixin W, Xingle A, et al. COVID-19 CT Lung and Infection Segmentation Dataset [Data set]. Zenodo. 2020. Available from: https://doi.org/10.5281/zenodo.3757476

Karimpanal TG, Bouffanais R. Self-organizing maps for storage and transfer of knowledge in reinforcement learning. Adapt Behav [Internet]. 2019;27(2):111–126. Available from: https://doi.org/10.1177%2F1059712318818568

Nefoussi S, Amamra A, Amarouche IA. A Comparative Study of Deep Learning Networks for COVID-19 Recognition in Chest X-ray Images. In 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH) [Internet]. Boumerdes: IEEE; 2021:237–41. Available from: https://doi.org/10.1109/IHSH51661.2021.9378703

Shazia A, Xuan ZT, Chuah JH, Usman J, et al. A comparative study of multiple neural network for detection of COVID-19 on chest X-ray. EURASIP J Adv Signal Process [Internet]. 2021;2021(1):50. Available from: https://doi.org/10.1186/s13634-021-00755-1

Perumal, V, Narayanan V, Rajasekar SJS. Detection of COVID-19 using CXR and CT images using Transfer Learning and Haralick features. Appl Intell [Internet]. 2021;51:341–358. Available from: https://doi.org/10.1007/s10489-020-01831-z

Rahaman MM, Li C, Yao Y, Kulwa F, et al. Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches. J Xray Sci Technol [Internet]. 2020;28(5):821–39. Available from: https://doi.org/10.3233/xst-200715

He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Internet]. Las Vegas: IEEE; 2016:770-778. Available from: https://doi.org/10.1109/CVPR.2016.90

Simonyan K, Zisserman A. Very Deep Convolutional Networks For Large-Scale Image Recognition. arXiv:1409.1556 [Internet]. 2015. Available from: arXiv:1409.1556v6

Szegedy C, Ioffe S, Vanhoucke V, Alemi AA. Inception-v4, inception-ResNet and the impact of residual connections on learning. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence [Internet]. San Francisco:AAAI Pres; 2017:4278–4284. Available from: https://dl.acm.org/doi/10.5555/3298023.3298188

Zoph B, Brain G, Vasudevan V, Shlens J, Le Google Brain Q V. Learning Transferable Architectures for Scalable Image Recognition. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition [Internet]. Salt Lake City :IEEE; 2018:8697-8710. Available from: https://doi.org/10.1109/CVPR.2018.00907

Sahlol AT, Kollmannsberger P, Ewees AA. Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features. Sci Rep [Internet]. 2020;10(1):2536. Available from: https://doi.org/10.1038/s41598-020-59215-9

Russakovsky O, Deng J, Su H, Krause J, et al. ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis [Internet]. 2015;115:211–52. Available from: https://doi.org/10.1007/s11263-015-0816-y

Ahuja S, Panigrahi BK, Dey N, Rajinikanth V, et al. Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Appl Intell [Internet]. 2021;51:571–585. Available from: https://doi.org/10.1007/s10489-020-01826-w

Dey N, Rajinikanth V, Fong SJ, Kaiser MS, et al. Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images. Cogn Comput [Internet]. 2020;12:1011–1023. Available from: https://doi.org/10.1007/s12559-020-09751-3

Franquet, T. Diagnóstico por imagen de las enfermedades Pulmonares difusas: Signos y patrones diagnósticos básicos. Med respir. 2012;5(3):49-67

Downloads

Published

2022-04-13

How to Cite

Arreola Minjarez, J. I., Díaz Román, J. D., Mederos Madrazo, B. J., Mejía Muñoz, J. M. ., Rascón Madrigal, L. H., & Cota Ruiz, J. de D. . (2022). Detection of COVID-19 Lung Lesions in Computed Tomography Images Using Deep Learning. Revista Mexicana De Ingenieria Biomedica, 43(1), 7–18. https://doi.org/10.17488/RMIB.43.1.1

Issue

Section

Research Articles

Dimensions Citation