Abstract
During the COVID-19 pandemic, the conventional RT-PCR method for diagnosing SARS-CoV-2 was limited by its processing time and accuracy rate. Consequently, thoracic CT images have been explored as a reliable and accurate alternative solution. However, analyzing a large volume of CT images by radiologists remains a major challenge. Deep learning solutions, specifically convolutional neural networks (CNN), have proven useful in radiology for image classification, object detection, semantic segmentation, and instance segmentation. In this study, DL approaches were evaluated for the automatic detection of COVID-19 on thoracic CT images using advanced deep networks such as XCEPTION, VGG, RESNET, and INCEPTION. A new advanced deep architecture called “VGG-advanced-Model” was also proposed, which improved upon the original VGG architecture that had achieved the best results. Various data preparation and preprocessing techniques were combined to enhance performance. The results demonstrate the effectiveness and efficiency of our method, achieving 100% precision, 99.52% accuracy, and 99% recall.
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
Kanumuri, C., Madhavi, C.R., Ravichandra, T.: Novel approach in classification and prediction of COVID-19 from radiograph images using CNN. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 13(9) (2022)
El Biach, F.Z., Imad, I.A.L.A., Laanaya, H., Minaoui, K.: CovSeg-Unet: End-to-End method-based Computer-Aided decision support system in lung COVID-19 detection on CT image. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 13(1) (2022)
Debelee, T.G., Schwenker, F., Ibenthal, A., Yohannes, D.: Survey of deep learning in breast cancer image analysis. Evol. Syst.. Syst. 11(1), 143–163 (2020). https://doi.org/10.1007/s12530-019-09297-2
Bai, J., Posner, R., Wang, T., Yang, C., Nabavi, S.: Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: a review. Med. Image Anal. 71, 102049 (2021). https://doi.org/10.1016/j.media.2021.102049
Tariq, N., Abid, B., Qadeer, K.A., Hashim, I., Ali, Z., Khosa, I.: Breast Cancer Classification using Global Discriminate Features in Mammographic Images. Int. J. Adv. Comput. Sci. Appl. 10(2) (IJACSA) (2019)
Nawaz, M., Sewissy, A.A., Soliman, T.H.A.: Multi-class breast cancer classification using deep learning convolutional neural network. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 9(6), 316–332 (2018)
Jinnai, S., Yamazaki, N., Hirano, Y., Sugawara, Y., Ohe, Y., Hamamoto, R.: The development of a skin cancer classification system for pigmented skin lesions using deep learning. Biomolecules 10(8), 1123 (2020). https://doi.org/10.3390/biom10081123
Mohsen, H., El-Dahshan, E.-S.A., El-Horbaty, E.-S.M., Salem, A.-B.M.: Classification using deep learning neural networks for brain tumors. Futur. Comput. Inform. J. 3(1), 68–71 (2018). https://doi.org/10.1016/j.fcij.2017.12.001
Bharati, S., Podder, P., Mondal, M.R.H.: Hybrid deep learning for detecting lung diseases from X-ray images Informatics Med. Unlocked 20, 100391 (2020). https://doi.org/10.1016/j.imu.2020.100391
Arai, K., Herdiyeni, Y., Okumura, H.: Comparison of 2D and 3D local binary pattern in lung cancer diagnosis. Int. J. Adv. Comput. Sci. Appl. (IJACSA). 3(4) (2012)
Geetha. N, Sathish Aaron, S. J., Joseph S.J: Deep separable convolution network for prediction of lung diseases from X-rays. Int. J. Adv. Comput. Sci. Appl. (IJACSA). 13(6) (2022)
Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M.: A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). Eur. Radiol.Radiol. 31(8), 6096–6104 (2021). https://doi.org/10.1007/s00330-021-07715-1
Alshazly, H., Linse, C., Barth, E., Martinetz, T.: Explainable COVID-19 detection using chest CT scans and deep learning. Sensors 21(2), 455 (2021). https://doi.org/10.3390/s21020455
Irmak, E.: A novel deep convolutional neural network model for COVID-19 disease detection. In: Medical Technologies Congress (TIPTEKNO), pp. 1–4 (2020)
Kim, D., Seo, S.B., Yoo, N.H., Shin, G.: A study on sample size sensitivity of factorymanufacturing dataset for CNN-based defective product classification. Computation 10, 142 (2022). https://doi.org/10.3390/computation10080142
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618–626 (2017)
Torrey, L., Shavlik, J.: Transfer Learning. Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques Emilio Soria Olivas IGI Global, pp. 242–264 (2010)
CNCSTR of Morocco 2022.: The high-performance computing platform (HPC). https://hpc.marwan.ma/ Accessed 11 May 2022
Alzubaidi, L., et al. : Review of deep learning: concepts, CNN architectures, challenges, applications, future directions Journal of Big Data. 8(1), 53 (2021). https://doi.org/10.1186/s40537-021-00444-8
Selvaraju, R.R., Das, A., Vedantam, R., Cogswell, M., Parikh, D.: Grad-CAM: Why did you say that? ArXiv abs/1611.07450
Hassantabar, S., Ahmadi, M., Sharifi, A.: Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches. Chaos Solitons Fractals 140, 110170 (2020). https://doi.org/10.1016/j.chaos.2020.110170
Acknowledgments
This work was supported by the “Urgence COVID-19” fundraising campaign of the Institut Pasteur.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Millimono, S. et al. (2024). VGG-AM: Towards a New Hybrid Medical Imaging Analysis Based on VGG Classification Model and Deep DATA Preparation. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 904. Springer, Cham. https://doi.org/10.1007/978-3-031-52388-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-52388-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-52387-8
Online ISBN: 978-3-031-52388-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)