- Oumar Khlelfa12,
- Aymen Yahyaoui12,13,
- Mouna Ben Azaiz14,
- Anwer Ncibi12,
- Ezzedine Gazouani14,
- Adel Ammar15 &
- …
- Wadii Boulila15,16
Part of the book series:Communications in Computer and Information Science ((CCIS,volume 1864))
Included in the following conference series:
Abstract
Nowadays, diseases are increasing in numbers and severity by the hour. Immunity diseases, affecting 8% of the world population in 2017 according to the World Health Organization (WHO), is a field in medicine worth attention due to the high rate of disease occurrence classified under this category. This work presents an up-to-date review of state-of-the-art immune diseases healthcare solutions. We focus on tackling the issue with modern solutions such as Deep Learning to detect anomalies in the early stages hence providing health practitioners with efficient tools. We rely on advanced deep learning techniques such as Convolutional Neural Networks (CNN) to fulfill our objective of providing an efficient tool while providing a proficient analysis of this solution. The proposed solution was tested and evaluated by the immunology department in the Principal Military Hospital of Instruction of Tunis, which considered it a very helpful tool.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 13727
- Price includes VAT (Japan)
- Softcover Book
- JPY 17159
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bagatini, M.D., et al.: Immune system and chronic diseases 2018. J. Immunol. Res. (2018)
Van Hoovels, L., et al.: Variation in antinuclear antibody detection by automated indirect immunofluorescence analysis. Ann. Rheum. Dis.78(6), e48–e48 (2019)
Cascio, D., et al.: Deep CNN for IIF images classification in autoimmune diagnostics. Appl. Sci.9(8), 1618 (2019)
Jain, S., et al.: Role of direct immunofluorescence microscopy in spectrum of diffuse proliferative glomerulonephritis: a single-center study. J. Microsc. Ultrastruct.9(4), 177 (2021)
Boyer, O., Candon, S.: Autoimmune diseases: The breakdown of self-tolerance (2021).https://www.inserm.fr/information-en-sante/dossiers-information/maladies-auto-immunes
Shen, L., Lin, J.: HEp-2 image classification using intensity order pooling based features and bag of words. Pattern Recognit.47(7), 2419–2427 (2014)
Liu, L., Wang, L.: HEp-2 cell image classification with multiple linear descriptors. Pattern Recognit.47(7), 2400–2408 (2014)
Qawqzeh, Y., Bajahzar, A., Jemmali, M., Otoom, M., Thaljaoui, A.: Classification of diabetes using photoplethysmogram (PPG) waveform analysis: logistic regression modeling. BioMed Res. Int. 2020 (2020)
Driss, K., Boulila, W., Batool, A., Ahmad, J.: A novel approach for classifying diabetes’ patients based on imputation and machine learning. In: 2020 International Conference On UK-China Emerging Technologies (UCET), pp. 1–4 (2020)
Al-Sarem, M., Alsaeedi, A., Saeed, F., Boulila, W., AmeerBakhsh, O.: A novel hybrid deep learning model for detecting COVID-19-related rumors on social media based on LSTM and concatenated parallel CNNs. Appl. Sci.11, 7940 (2021)
Al-Sarem, M., Saeed, F., Boulila, W., Emara, A.H., Al-Mohaimeed, M., Errais, M.: Feature selection and classification using CatBoost method for improving the performance of predicting Parkinson’s disease. In: Saeed, F., Al-Hadhrami, T., Mohammed, F., Mohammed, E. (eds.) Advances on Smart and Soft Computing. AISC, vol. 1188, pp. 189–199. Springer, Singapore (2021).https://doi.org/10.1007/978-981-15-6048-4_17
Ben Atitallah, S., Driss, M., Boulila, W., Ben Ghezala, H.: Randomly initialized convolutional neural network for the recognition of COVID-19 using X-ray images. Int. J. Imaging Syst. Technol.32, 55–73 (2022)
Rasool, M., Ismail, N., Boulila, W., Ammar, A., Samma, H., Yafooz, W., Emara, A.: A hybrid deep learning model for brain tumour classification. Entropy24, 799 (2022)
Jemmali, M., Melhim, L., Alourani, A., Alam, M.: Equity distribution of quality evaluation reports to doctors in health care organizations. PeerJ Comput. Sci.8, e819 (2022)
Alam, M., Melhim, L., Ahmad, M., Jemmali, M.: Public attitude towards covid-19 vaccination: validation of covid-vaccination attitude scale (c-vas). J. Multidisc. Healthc., 941–954 (2022)
https://www.abcam.com/secondary-antibodies/direct-vs-indirect-immunofluorescence
Han, X.-H., Lei, J., Chen, Y.-W.: HEp-2 cell classification using k-support spatial pooling in deep CNNs. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 3–11. Springer, Cham (2016).https://doi.org/10.1007/978-3-319-46976-8_1
Phan, H.T.H., et al.: Transfer learning of a convolutional neural network for HEp-2 cell image classification. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1208–1211. IEEE (2016)
Lu, M., et al.: Hep- 2 cell image classification method based on very deep convolutional networks with small datasets. In: Ninth International Conference on Digital Image Processing (ICDIP 2017), vol. 10420, p. 1042040. Inter- national Society for Optics and Photonics (2017)
Benammar Elgaaied, A., et al.: Computer-assisted classification patterns in autoimmune di- agnostics: the AIDA project. BioMed Res. Int. (2016)
Vununu, C., Lee, S.-H., Kwon, O.-J., Kwon, K.-R.: A dynamic learning method for the classification of the hep-2 cell images. Electronics8(8), 850 (2019)
Bayramoglu, N., et al.: Human epithelial type 2 cell classification with convolutional neural networks. In: 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 1–6. IEEE (2015)
Rodrigues, L.F., et al.: HEp-2 cell image classification based on convolutional neural networks. In: 2017 Workshop of Computer Vision (WVC), pp. 13–18. IEEE (2017)
Rodrigues, L.F., et al.: Comparing convolutional neural networks and preprocessing techniques for hep-2 cell classification in immunofluorescence images. Comput. Biol. Med.116, 103542 (2020)
Majtner, T., Bajić, B., Lindblad, J., Sladoje, N., Blanes-Vidal, V., Nadimi, E.S.: On the effectiveness of generative adversarial networks as HEp-2 image augmentation tool. In: Felsberg, M., Forssén, P.-E., Sintorn, I.-M., Unger, J. (eds.) SCIA 2019. LNCS, vol. 11482, pp. 439–451. Springer, Cham (2019).https://doi.org/10.1007/978-3-030-20205-7_36
Li, H., et al.: Deep CNNs for hep-2 cells classification: A cross-specimen analysis. arXiv preprintarXiv:1604.05816 (2016)
Lei, H., et al.: A deeply supervised residual network for hep-2 cell classification via cross-modal transfer learning. Pattern Recognit.79, 290–302 (2018)
Acknowledgment
The authors would like to thank Prince Sultan University for financially supporting the conference attendance fees.
Author information
Authors and Affiliations
Military Academy of Fondouk Jedid, 8012, Nabeul, Tunisia
Oumar Khlelfa, Aymen Yahyaoui & Anwer Ncibi
Science and Technology for Defense Lab (STD), Ministry of National Defense, Tunis, Tunisia
Aymen Yahyaoui
Immunology Department, The Principal Military Hospital of Instruction of Tunis, Tunis, Tunisia
Mouna Ben Azaiz & Ezzedine Gazouani
Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh, Saudi Arabia
Adel Ammar & Wadii Boulila
RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba, Tunisia
Wadii Boulila
- Oumar Khlelfa
You can also search for this author inPubMed Google Scholar
- Aymen Yahyaoui
You can also search for this author inPubMed Google Scholar
- Mouna Ben Azaiz
You can also search for this author inPubMed Google Scholar
- Anwer Ncibi
You can also search for this author inPubMed Google Scholar
- Ezzedine Gazouani
You can also search for this author inPubMed Google Scholar
- Adel Ammar
You can also search for this author inPubMed Google Scholar
- Wadii Boulila
You can also search for this author inPubMed Google Scholar
Corresponding authors
Correspondence toOumar Khlelfa,Aymen Yahyaoui orWadii Boulila.
Editor information
Editors and Affiliations
Wrocław University of Science and Technology, Wrocław, Poland
Ngoc Thanh Nguyen
Eötvös Loránd University, Budapest, Hungary
János Botzheim
Eötvös Loránd University, Budapest, Hungary
László Gulyás
Universidad Complutense de Madrid, Madrid, Spain
Manuel Nunez
Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Jan Treur
University of Münster, Münster, Germany
Gottfried Vossen
Wrocław University of Science and Technology, Wrocław, Poland
Adrianna Kozierkiewicz
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Khlelfa, O.et al. (2023). Interpretation of Immunofluorescence Slides by Deep Learning Techniques: Anti-nuclear Antibodies Case Study. In: Nguyen, N.T.,et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_9
Download citation
Published:
Publisher Name:Springer, Cham
Print ISBN:978-3-031-41773-3
Online ISBN:978-3-031-41774-0
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative