Enhanced deep learning based on Fusion data to diagnosis malignancy Thyroid tumour
- DOI
- 10.2991/978-94-6463-496-9_15How 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.
- Copyright
- © 2024 The Author(s)
- 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
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 -