Feature selection of Thyroid disease using Deep Learning: A Literature survey

Abstract

The thyroid hormone, which is secreted by the thyroid gland, helps regulate the body's metabolism. Thyroid disorders can range from a small, harmless goiter that does not need to be treated for life-threatening cancer. The most common thyroid problems include abnormal production of thyroid hormones. Overproduction of the thyroid leads to the thyroid and inadequate hormone production leads to hypothyroidism. Although the effects can be unpleasant or uncomfortable, many thyroid problems can be managed well if they are timely diagnosed and treated correctly. In this paper, the diagnosis of thyroid disease is investigated using deep learning based on the imperialist competitive algorithm feature selection method.

Keywords

• Thyroid disorders,Diagnosis,Deep Learning,feature selection method,imperialist competitive algorithm

References

1. Siti F, Shurehdeli MA, Teshneh Lab M. 2008. Diagnosis of thyroid disease using probabilistic neural networks and Genetic Algorithm: 2nd Joint Congress on Fuzzy and Intelligent Systems. Iran.
2. Razmjooy N, Musavi BS, Soleymani F. 2013. A hybrid neural network Imperialist Competitive Algorithm for skin color segmentation. Mathematical and Computer Modelling 57(3): 848-856.
3. Sarigül M, Özyildirim BM, Avci M. 2019. Differential Convolutional Neural Network, Neural Networks, 116:279-287.
4. Zhou T, Ruan S, Canu S. 2019. A review: Deep learning for medical image segmentation using multi-modality fusion. Array 3-4: 100004.
5. Seaver N. 2014. Media in Transition 8, Cambridge, MA, April.Knowing algorithms.Department of Anthropology, UC Irvine Intel Science and Technology Center for Social Computing. 1:23-28
6. Memari A, Robiah A, Abdul Rahim Abd. 2017. Metaheuristic Algorithms: Guidelines for Implementation. Journal of Soft Computing and Decision Support Systems. 4: 1-6.
7. Rajpurohit J, Sharma TK, Abraham A, Vaishali. 2017. Glossary of Metaheuristic Algorithms. International Journal of Computer Information Systems and Industrial Management Applications, 9: 181-205.
8. Abdi B, Mozafari H, Ayob A, Kohandel R. 2011. Imperialist Competitive Algorithm and its Application in Optimization of Laminated Composite Structures. European Journal of Scientific Research ISSN. 55: 1450-216.

9. Mousavirad SJ, Akhlaghian Tab F, Mollazade K. 2012. Application of Imperialist Competitive Algorithm for Feature Selection: A Case Study on Bulk Rice Classification. International Journal of Computer Applications. 40: 41-48.
10. Magsudi M, Gazvini M. 2018. Metadata algorithms for dimensionality reduction and feature selection 3rd International Conference on Combination, Cryptography and Computing, Iran University of Science and Technology.
11. Ha VS, Nguyen HN. 2016. Credit scoring with a feature selection approach based deep learning. MATEC Web of Conferences 54: 05004. 10.1051/matecconf/20165405004.
12. Roy D, Kodukula SRM, Chalavadi KM. 2015. Feature selection using Deep Neural Networks. 1: 1-6.
13. Taherkhani A, Cosma G, McGinnity TM. 2018. Deep-FS: A feature selection algorithm for Deep Boltzmann Machines. Neurocomputing 322: 22-37.
14. Musavirad SJ, Ebrahimpoor-KomlehH. 2013. Feature selection using modified imperialist competitive algorithm, ICCKE 2013, Mashhad, 2013, pp. 400-405 10.1109/ICCKE.2013.6682833 Nama S. 2013. REVIEW ON THYROID DISORDERS.
15. Kumar V, Minz S. 2014. Feature selection: A literature review. Smart Computing Review. 4: 211-229.
16. LeCun Y, Bengio Y, Hinton G. 2015.Deep Learning. Nature. 521: 436-444.
17. Schmidhuber J. (2014). Deep Learning in Neural Networks: An Overview. Neural Networks. 61. 10.1016/j.neunet.2014.09.003.
18. Cormen TH, Leiserson ChE, Rivest RL, Stein C. 2009. Introduction to Algorithms, 3rd Edition The MIT Press, ISBN 978-0-262-03384-8 hardcover : alk. paper—ISBN 978-0-262-53305-8 pbk. : alk. paper
19. Razia Sh, Rao M. 2016. Machine Learning Techniques for Thyroid Disease Diagnosis:A Review. Indian Journal of Science and Technology. 9(28): 1-9.
20. Bakator M, Radosav D. 2018. Deep Learning and Medical Diagnosis: A Review of Literature. Multimodal Technologies and Interaction. 2(3): 47-51. Liu X, Faes L, Kale A, Wagner S, Fu D, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C, Ledsam J, Schmid M, Balaskas K, Topol E, Bachmann L, Keane P, Denniston A. 2019. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. 1. 10.1016/S2589-7500(19)30123-2.The lancet digital health 1(6): 271-297.