The Role of Machine Learning in Thyroid Cancer Diagnosis
- DOI
- 10.2991/978-94-6463-136-4_25How to use a DOI?
- Keywords
- Machine learning (ML); SVM; Segmentation
- Abstract
Thyroid cancer occurs in the thyroid gland cells. This is butterfly shaped gland which is situated at lower part of neck. Thyroid cancer might not cause any symptoms initially. But as the cancer grows, it causes pain and swelling in neck. To detect and classify abnormalities of the thyroid gland Ultrasound imaging is mostly used. Computer aided diagnosis (CAD) help healthcare sector to increase the diagnosis accuracy and to reduce biopsy ratio. Machine learning techniques play a vital role in diagnosing diseases from the medical database. Various diseases can be predicted early using machine learning techniques. In this study, many previous research works are reviewed which use machine learning techniques to predict thyroid cancer. Machine learning classification techniques like Decision tree Classification, Random Forest Classification, Naïve Bayes, Kernel SVM, K-Nearest Neighbours, Support vector Machines (SVM), Logistic regression etc. are reviewed from the research papers. A short description about machine learning and segmentation is also presented.
- Copyright
- © 2023 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 - Monika D. Kate AU - Vijay Kale PY - 2023 DA - 2023/05/01 TI - The Role of Machine Learning in Thyroid Cancer Diagnosis BT - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022) PB - Atlantis Press SP - 276 EP - 287 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-136-4_25 DO - 10.2991/978-94-6463-136-4_25 ID - Kate2023 ER -