Abstract
The Covid-19 pandemic is a universal problem that has caused significant outbreaks in every country and region, affecting men and women of all ages around the world. The automatic detection of lung infection is a major challenge that poses a limitation to the potential medical imaging offers to augment patient treatments and strategies for tackling the impact of Covid-19. One of the best and fastest way to diagnose this virus on a patient is to detect it on lung computed tomography (CT) scan images. Although, to find the tissues that are infected and segmenting them on the CT scan images face many challenges. To overcome these challenges, a method was created to enhance the slides on the CT scans, then a region of interest in which the lung was cropped out of the CT images to reduce the noise of the dataset before fitting it into the model for training. Due to the small amount of data, a method was utilized for data augmentation to overcome the problem of overfitting. After compiling the Unet model on the dataset and evaluating the model metrics, the results and the output that was generated show that the model achieved good results. The model achieved an Accuracy of approximately 96%, Intersection over Union (IoU) of approximately 85%, Dice Similarity Coefficient of approximately 92%, Precision of 92%, Recall of 93%, F1 score of 93%, and Loss of -85%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmadi, M., Sharifi, A., Dorosti, S., Ghoushchi, S.J., Ghanbari, N.: Investigation of effective climatology parameters on covid-19 outbreak in Iran. Sci. Total Environ. 729, 138705 (2020)
Ai, T., et al.: Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (covid-19) in China: a report of 1014 cases. Radiology 296, E32–E40 (2020)
Bhandary, A.: Deep-learning framework to detect lung abnormality-a study with chest x-ray and lung CT scan images. Pattern Recogn. Lett. 129, 271–278 (2020)
Chen, Y., Sohel, F., Shah, S.A.A., Ding, S.: Deep Boltzmann machine for corrosion classification using eddy current pulsed thermography. Optik 219, 164828 (2020)
Elnakib, A., Gimel’farb, G., Suri, J.S., El-Baz, A.: Medical image segmentation: a brief survey. In: El-Baz, A.S., Acharya U, R., Laine, A.F., Suri, J.S. (eds.) Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies, pp. 1–39. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-8204-9_1
Fan, D.P., Zhou, T., Ji, G.P., Zhou, Y., Chen, G., Fu, H., Shen, J., Shao, L.: Inf-net: automatic covid-19 lung infection segmentation from CT images. IEEE Trans. Med. Imaging 39(8), 2626–2637 (2020)
Gu, Y., et al.: Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach. Pattern Recogn. 46(3), 692–702 (2013)
Hamzenejad, A., Jafarzadeh Ghoushchi, S., Baradaran, V., Mardani, A.: A robust algorithm for classification and diagnosis of brain disease using local linear approximation and generalized autoregressive conditional heteroscedasticity model. Mathematics 8(8), 1268 (2020)
Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., Kaur, M.: Classification of the covid-19 infected patients using densenet201 based deep transfer learning. J. Biomolecular Struct. Dyn. 39(15), 5682–5689 (2021)
Lozej, J., Meden, B., Struc, V., Peer, P.: End-to-end iris segmentation using u-net. In: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), pp. 1–6. IEEE (2018)
Oulefki, A., Agaian, S., Trongtirakul, T., Laouar, A.K.: Automatic covid-19 lung infected region segmentation and measurement using ct-scans images. Pattern Recogn. 114, 107747 (2021)
Ouyang, W., Xu, B., Yuan, X.: Color segmentation in multicolor images using node-growing self-organizing map. Color Res. Appl. 44(2), 184–193 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Shan, F., et al.: Abnormal lung quantification in chest CT images of covid-19 patients with deep learning and its application to severity prediction. Med. Phys. 48(4), 1633–1645 (2021)
Tiwari, S., et al.: Applications of machine learning approaches to combat covid-19: a survey. In: Lessons from COVID-19 pp. 263–287 (2022)
Wang, X., et al.: A weakly-supervised framework for covid-19 classification and lesion localization from chest CT. IEEE Trans. Med. Imaging 39(8), 2615–2625 (2020)
Xu, X., et al.: A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering 6(10), 1122–1129 (2020)
Yuan, X.: Segmentation of blurry object by learning from examples. In: Medical Imaging 2010: Image Processing, vol. 7623, pp. 1379–1387. SPIE (2010)
Yuan, X., Xie, L., Abouelenien, M.: A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data. Pattern Recogn. 77, 160–172 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Akinlade, O., Vakaj, E., Dridi, A., Tiwari, S., Ortiz-Rodriguez, F. (2023). Semantic Segmentation of the Lung to Examine the Effect of COVID-19 Using UNET Model. In: Jabbar, M.A., Ortiz-Rodríguez, F., Tiwari, S., Siarry, P. (eds) Applied Machine Learning and Data Analytics. AMLDA 2022. Communications in Computer and Information Science, vol 1818. Springer, Cham. https://doi.org/10.1007/978-3-031-34222-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-34222-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34221-9
Online ISBN: 978-3-031-34222-6
eBook Packages: Computer ScienceComputer Science (R0)