Lung CT Image Segmentation Using VGG-16 Network with Image Enhancement Based on Bounded Turning Mittag-Leffler Function

Authors

  • Ali M. Hasan Department of Physiology and Medical Physics, College of Medicine, Al-Nahrain University, Baghdad, Iraq. https://orcid.org/0000-0001-9151-6958
  • Mohammed Khalaf Department of Computer Science, Al-Maarif University College, Al Anbar, Iraq.
  • Bayan M. Sabbar College of Engineering and Engineering Techniques, Al-Mustaqbal University, Babylon, Iraq.
  • Rabha W. Ibrahim Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.
  • Hamid A. Jalab Information and Communication Technology Research Group, Scientific Research Center, Alayen University, Thi Qar, Iraq.
  • Farid Meziane Data Science Research Centre, School of Computing and Engineering, University of Derby, UK.

DOI:

https://doi.org/10.21123/bsj.2024.9286

Keywords:

تحول محدود, الأشعة المقطعية, تحسين الصورة, وظيفة Mittag-Leffler , شبكة VGG-16.

Abstract

Automated segmentation of diseases considered a necessary initial step in routine diagnosis. Lung diseases that affect the lungs, such as pneumonia or lung collapse can result in areas of consolidation or atelectasis, where lung tissue becomes denser or collapses. It can be challenging to accurately segment such areas, as they may exhibit similar characteristics to adjacent structures. This study proposes a lung CT image segmentation method which includes two steps: (1) a new image enhancement model that uses the bounded turning Mittag-Leffler function to improve the CT images for better segmentation outcomes. (2) a new modified VGG-16 for infection of lung segmentation based on expanding the original VGG-16 network. The dilated convolutional layers are added to the original VGG-16 network to create a new lung CT image segmentation method. The experimental results showed that the proposed method can accurately segment the infected region in lung CT scans. The results led to Accuracy, Dice Coefficient and Jaccard Index values of 96.3%, 91.2%, and 82.3% respectively. The proposed method is accurate and suitable for implementation in real-world applications. Following result computation, seven related studies are compared with the recommended methodology. This demonstrated how well this study had performed in comparison to many earlier studies. Despite the fact that segmenting lung CT images requires a lot of work. Obtaining a suitable level of accuracy was quite difficult.

References

Salama GM., Mohamed A., Abd-Ellah MK. COVID-19 classification based on a deep learning and machine learning fusion technique using chest CT images. Neural Comput & Applic. 2024; 36, 5347–5365. https://doi.org/10.1007/s00521-023-09346-7.

Aggarwal P., Mishra NK., Fatimah B., Singh P., Gupta A., Joshi SD. COVID-19 image classification using deep learning: Advances, challenges and opportunities. Comput Biol Med. 2022; 144, 105350. https://doi.org/10.1016/j.compbiomed.2022.105350.

Hasan AM, Qasim AF, Jalab HA, Ibrahim RW. Breast Cancer MRI Classification Based on Fractional Entropy Image Enhancement and Deep Feature Extraction. Baghdad Sci J. 2022; 0221-0221. https://doi.org/10.21123/bsj.2022.6782.

Fadhil OY, Mahdi BS, Abbas AR. Using VGG Models with Intermediate Layer Feature Maps for Static Hand Gesture Recognition. Baghdad Sci J. 2023. https://doi.org/10.21123/bsj.2023.7364

Ibrahim RW, Jalab HA, Karim FK, Alabdulkreem E, Ayub MN. A medical image enhancement based on generalized class of fractional partial differential equations. Quant Imaging Med Surg. 2022; 12 (1): 172 . https://dx.doi.org/10.21037/qims-21-15

Cao J. An Image Enhancement Method Based on Fractional Calculus and Retinex. JCC. 2018; 6 (11), pp.55-65. http://doi.org/10.4236/jcc.2018.611005.

Yu J., Tan L., Zhou S., Wang L., Siddique MA. Image denoising algorithm based on entropy and adaptive fractional order calculus operator. IEEE access. 2017; 5: 12275-12285.

https://doi.org/10.1109/ACCESS.2017.2718558.

Kumar PA., Gunasundari R., Aarthi R. RE-SHFC: Renyi Entropy-Based Spotted Hyena Fractional Calculus Algorithm for MR Image Reconstruction. Sensing and Imaging. 2022; 23(1): pp. 8. https://doi.org/10.1007/s11220-022-00377-3

Gupta M., Mishra A. A systematic review of deep learning based image segmentation to detect polyp. Artif Intell Rev. 2024; 57 (7). https://doi.org/10.1007/s10462-023-10621-1

Alnedawe SM, Aljobouri HK. A New Model Design for Combating COVID-19 Pandemic Based on SVM and CNN Approaches. Baghdad Sci J. 2023; 20 (4): 1402-1413. https://doi.org/10.21123/bsj.2023.7403

Gite S., Mishra A., Kotecha K. Enhanced lung image segmentation using deep learning. Neural Comput & Applic. 2023; 35, 22839–22853. https://doi.org/10.1007/s00521-021-06719-8

Fan D-P, Zhou T, Ji G-P, Zhou Y, Chen G, Fu H, et al. Inf-net: Automatic covid-19 lung infection segmentation from ct images. IEEE Trans Med Imaging. 2020; 39 (8): 2626-2637. https://doi.org/10.1109/TMI.2020.2996645

Shan F, Gao Y, Wang J, Shi W, Shi N, Han M, et al. Lung infection quantification of COVID-19 in CT images with deep learning. arXiv preprint arXiv:2003.2020; 04655 . https://doi.org/10.48550/arXiv.2003.04655

Saood A, Hatem I. COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med Imaging 2021; 21 (1): 1-10. https://doi.org/10.1186/s12880-020-00529-5

Budak Ü, Çıbuk M, Cömert Z, Şengür A. Efficient COVID-19 segmentation from CT slices exploiting semantic segmentation with integrated attention mechanism. J Digit Imaging .2021; 34: 263-272. https://doi.org/10.1007/s10278-021-00434-5

Raj ANJ, Zhu H, Khan A, Zhuang Z, Yang Z, Mahesh VG, et al. ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans. PeerJ Comput Sci.2021; 7: e349. https://doi.org/10.7717/peerj-cs.349

Le V-L, Saut O, editors. RRc-UNet 3D for Lung Tumor Segmentation from CT Scans of Non-Small Cell Lung Cancer Patients. ICCV CVAMD 2023 - Workshop of International Conference on Computer Vision, Oct 2023, Paris, France.2023; pp.2316-2325. https://doi.org/10.1109/ICCVW60793.2023.00245

Liu C, Pang M. Lung CT Image Segmentation via Dilated U-Net Model and Multi-scale Gray Correlation-Based Approach. Circuits Syst Signal Process.2023; 1-18. https://doi.org/10.1007/s00034-023-02532-x

Petráš I. Novel Low-Pass Two-Dimensional Mittag–Leffler Filter and Its Application in Image Processing. Fractal and Fractional. 2023; 7(12): pp. 881. https://doi.org/10.3390/fractalfract7120881

COVID-19, Medical Segmentation. 2021. https://www.kaggle.com/competitions/covid-segmentation/data.

Sharif PM, Nematizadeh M, Saghazadeh M, Saghazadeh A, Rezaei N. Computed tomography scan in COVID-19: a systematic review and meta-analysis. Pol. J. Radiol.Feb 2022;87:e1–23. https://doi.org/10.5114%2Fpjr.2022.112613

Singh V, Abdel-Nasser M, Pandey N, Puig D. Lunginfseg: Segmenting covid-19 infected regions in lung ct images based on a receptive-field-aware deep learning framework. Diagnostics. 2021; 11 (2): 158 . https://doi.org/10.3390/diagnostics11020158

Downloads

Issue

Section

article

How to Cite

1.
Lung CT Image Segmentation Using VGG-16 Network with Image Enhancement Based on Bounded Turning Mittag-Leffler Function. Baghdad Sci.J [Internet]. [cited 2024 Jun. 14];21(12). Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9286