Enhanced deep learning based on Fusion data to diagnosis malignancy Thyroid tumour

Bennadji Ziad; Terrissa Sadek Labib; Benmohammed Karima; Zerhouni Noureddine

doi:10.2991/978-94-6463-496-9_15

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Enhanced deep learning based on Fusion data to diagnosis malignancy Thyroid tumour

Authors

Bennadji Ziad¹^{, *}, Terrissa Sadek Labib², Benmohammed Karima³, Zerhouni Noureddine⁴

¹LESIA Laboratory, University of Mohamed Khider, Biskra, Algeria

²LINFI Laboratory, University of Mohamed Khider, Biskra, Algeria

³MPAC Laboratory, University of Constantine 3, Constantine, Algeria

⁴FEMTO-ST Institute, University Bourgogne Franche-Comte, Besançon, France

^*Corresponding author. Email: ziad.bennadji@univ-biskra.dz

Corresponding Author

Bennadji Ziad

Available Online 31 August 2024.

DOI: 10.2991/978-94-6463-496-9_15 How to use a DOI?
Keywords: Deep learning; Feature selection; Thyroid cancer diagnosis; Ultrasound radiomics features; Data Fusion
Abstract: The predominant type of cancer within the endocrine system is Thyroid Cancer (TC), with the majority falling under the category of low-risk tumour. However, the over-diagnosis and over-treatment of such conditions serve as primary factors contributing to a patient’s deteriorating state, heightening the risk of recurrence and potentially complicating future interventions. Consequently, these practices elevate mortality rates and hinder complete recovery. Our paper focuses on developing a robust neural network model that integrates ultrasound radiomics with clinical data to accurately diagnose malignant thyroid tumours, aiming to mitigate issues associated with misdiagnosis and over-diagnosis. Based on independent cohort testing, the model demonstrates outstanding performance metrics with values of 0.97, 0.99, 0.97, and 0.98 for accuracy, AUC, precision, and recall, respectively.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
Series: Advances in Intelligent Systems Research
Publication Date: 31 August 2024
ISBN: 978-94-6463-496-9
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-496-9_15 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Bennadji Ziad
AU  - Terrissa Sadek Labib
AU  - Benmohammed Karima
AU  - Zerhouni Noureddine
PY  - 2024
DA  - 2024/08/31
TI  - Enhanced deep learning based on Fusion data to diagnosis malignancy Thyroid tumour
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 185
EP  - 198
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_15
DO  - 10.2991/978-94-6463-496-9_15
ID  - Ziad2024
ER  -

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