Skip to main content
SpringerLink
Log in

Computer-Aided Diagnosis of Thyroid Dysfunction: A Survey

  • Conference paper
  • First Online:
Big Data Analytics (BDA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12581))

Included in the following conference series:

Abstract

Thyroid is a hormone secreting gland that is crucial for the regulation of all the metabolic activities in our body. When the thyroid gland over-functions or under-functions, thyroid dysfunction occurs. In recent years, several computer-aided diagnosis techniques have been proposed in literature to assist doctors to diagnose thyroid dysfunction more accurately. Different techniques use different input modalities to diagnose thyroid dysfunction. Only one of the techniques uses the concept of deep learning to diagnose thyroid dysfunction and the rest of the techniques are machine learning based techniques. In this paper, we present a broad review of computer-aided thyroid dysfunction detection techniques. The paper is presented in five folds. Firstly, we discuss the various types of thyroid dysfunction. Second, we briefly illustrate clinical methods used to diagnose thyroid dysfunctions, along with the shortcomings of the clinical diagnostic process. Third, we discuss computer-aided thyroid dysfunction detection techniques and propose their classification based on the input modality used by them. Fourth, we present a summary of computer-aided techniques which reveals strengths, open research problems and scope of the improvement in this research area. Fifth, we identify a set of parameters to compare computer-aided techniques and present their comparison based on the identified parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mitra, S.: Thyroid anatomy and physiology of thyroid hormone secretion. Manage. Thyroid Disord. Made Easy. 1 (2009)

    Google Scholar 

  2. Dev, N., Sankar, J., Vinay, M.V.: Functions of thyroid hormones. Thyroid Disord. 11–25 (2016)

    Google Scholar 

  3. Khanorkar, S.: Functions of thyroid hormones and diseases of thyroid gland. Insights Physiol. 451 (2012)

    Google Scholar 

  4. Jolobe, O.M.P.: Thyroid disorders—an update. Postgrad. Med. J. 77(904), 144 (2001)

    Google Scholar 

  5. Monaco, F.: Classification of thyroid diseases: suggestions for a revision. J. Clin. Endocrinol. Metab. 88(4), 1428–1432 (2003)

    Article  Google Scholar 

  6. Galofré, J.C., Díez, J.J., Cooper, D.S.: Thyroid dysfunction in the era of precision medicine. Endocrinología y Nutrición 63(7), 354–363 (2016)

    Article  Google Scholar 

  7. Unnikrishnan, A., Menon, U.: Thyroid disorders in India: an epidemiological perspective. Indian J. Endocrinol. Metab. 15(6), 78 (2011)

    Article  Google Scholar 

  8. Mohamedali, M., Maddika, S.R., Vyas, A., Iyer, V., Cheriyath, P.: Thyroid disorders and chronic kidney disease. Int. J. Nephrol. 2014, 1–6 (2014)

    Article  Google Scholar 

  9. Sheehan, M.T.: Biochemical testing of the thyroid: TSH is the best and, oftentimes, only test needed – a review for primary care. Clin. Med. Res. 14(2), 83–92 (2016)

    Google Scholar 

  10. Koulouri, O., Moran, C., Halsall, D., Chatterjee, K., Gurnell, M.: Pitfalls in the measurement and interpretation of thyroid function tests. Best Pract. Res. Clin. Endocrinol. Metab. 27(6), 745–762 (2013)

    Article  Google Scholar 

  11. Fröhlich, E., Wahl, R.: Thyroid autoimmunity: role of anti-thyroid antibodies in thyroid and extra-thyroidal diseases. Front. Immunol. 8, 521 (2017)

    Article  Google Scholar 

  12. Ceccarini, G., Santini, F., Vitti, P.: Tests of thyroid function. Endocrinol. Thyroid Dis. 1–23 (2017)

    Google Scholar 

  13. Amdur, R.J., Mazzaferri, E.L.: Definitions: thyroid uptake measurement, thyroid scan, and wholebody scan. In: Amdur, R.J., Mazzaferri, E.L. (eds.) Essentials of Thyroid Cancer Management, pp. 49–54. Springer, Boston (2005). https://doi.org/10.1007/0-387-25714-4_6

  14. Caplan, R.H.: Thyroid uptake of radioactive iodine. JAMA 215(6), 916 (1971)

    Article  Google Scholar 

  15. Hegedü, L.: Thyroid Ultrasonography as a Screening Tool for Thyroid Disease. Thyroid. 14(11), 879–880 (2004)

    Article  Google Scholar 

  16. Chaudhary, V., Bano, S.: Thyroid ultrasound. Indian J. Endocrinol. Metab. 17(2), 219 (2013)

    Article  Google Scholar 

  17. Sholosh, B., Borhani, A.A.: Thyroid ultrasound part I: technique and diffuse disease. Radiol. Clin. North Am. 49(3), 391–416 (2011)

    Article  Google Scholar 

  18. Goodman, H.M.: Basic Medical Endocrinology. Elsevier, Oxford (2009)

    Google Scholar 

  19. Mense, M.G., Boorman, G.A.: Thyroid gland. Boorman’s Pathol. Rat. 669–686 (2018)

    Google Scholar 

  20. Beynon, M.E., Pinneri, K.: An overview of the thyroid gland and thyroid-related deaths for the forensic pathologist. Acad. Forensic Pathol. 6(2), 217–236 (2016)

    Article  Google Scholar 

  21. Galofré, J.C., Díez, J.J., Cooper, D.S.: Thyroid dysfunction in the era of precision medicine. Endocrinología y Nutrición 63(7), 354–363 (2016)

    Article  Google Scholar 

  22. Little, J.W.: Thyroid disorders. part i: hyperthyroidism. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endodontology 101(3), 276–284 (2006)

    Google Scholar 

  23. Leo, S.D., Lee, S.Y., Braverman, L.E.: Hyperthyroidism. The Lancet 388(10047), 906–918 (2016)

    Article  Google Scholar 

  24. Ross, D.S., et al.: 2016 American thyroid association guidelines for diagnosis and management of hyperthyroidism and other causes of thyrotoxicosis. Thyroid 26(10), 1343–1421 (2016)

    Article  Google Scholar 

  25. Gaitonde, D.Y., Rowley, K.D., Sweeney, L.B.: Hypothyroidism: an update. Am. Fam. Phys. 244–251 (2012)

    Google Scholar 

  26. Vanderpump, M.P.: Epidemiology of thyroid disease. In: Encyclopedia of Endocrine Diseases, pp. 486–495 (2018)

    Google Scholar 

  27. Taylor, P.N., et al.: Global epidemiology of hyperthyroidism and hypothyroidism. In: Yearbook of Paediatric Endocrinology (2018)

    Google Scholar 

  28. Medeiros-Neto, G., Camargo, R.Y., Tomimori, E.K.: Approach to and treatment of goiters. Med. Clin. N. Am. 96(2), 351–368 (2012)

    Article  Google Scholar 

  29. Dauksiene, D., et al.: Factors associated with the prevalence of thyroid nodules and goiter in middle-aged euthyroid subjects. Int. J. Endocrinol. 2017, 1–8 (2017)

    Article  Google Scholar 

  30. Mesele, M., Degu, G., Gebrehiwot, H.: Prevalence and associated factors of goiter among rural children aged 6–12 years old in Northwest Ethiopia, cross -sectional study. BMC Pub. Health. 14(1), 130 (2014)

    Google Scholar 

  31. Little, J.W.: Thyroid disorders. Part II: hypothyroidism and thyroiditis. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endodontology 102(2), 148–153 (2006)

    Google Scholar 

  32. Caturegli, P., Remigis, A.D., Rose, N.: Hashimoto thyroiditis: clinical and diagnostic criteria. Autoimmun. Rev. 13(4–5), 391–397 (2014)

    Article  Google Scholar 

  33. Pearce, E.N., Farwell, A.P., Braverman, L.E.: Thyroiditis. N. Engl. J. Med. 348(26), 2646–2655 (2003)

    Article  Google Scholar 

  34. Polyzos, S., Kita, M., Avramidis, A.: Thyroid nodules - stepwise diagnosis and management. Hormones 6(2), 101–119 (2007)

    Article  Google Scholar 

  35. Jibawi, A., Cade, D.: Thyroid nodules and cancer. In: Current Surgical Guidelines. pp. 389–398. (2009)

    Google Scholar 

  36. Durante, C., Grani, G., Lamartina, L., Filetti, S., Mandel, S.J., Cooper, D.S.: The diagnosis and management of thyroid nodules. JAMA 319(9), 914 (2018)

    Article  Google Scholar 

  37. Gimm, O.: Thyroid cancer. Cancer Lett. 163(2), 143–156 (2001)

    Article  Google Scholar 

  38. Sessions, R.B., Davidson, B.J.: Thyroid cancer. Med. Clin. N. Am. 77(3), 517–538 (1993)

    Article  Google Scholar 

  39. Yadav, D.C., Pal, S.: To generate an ensemble model for women thyroid prediction using data mining techniques. Asian Pac. J. Cancer Prevent. 20(4), 1275–1281 (2019)

    Article  Google Scholar 

  40. Begum, A., Parkavi, A.: Prediction of thyroid disease using data mining techniques. In: 5th International Conference on Advanced Computing & Communication Systems (2019)

    Google Scholar 

  41. Obeidavi, M.R., Rafiee, A., Mahdiyar, O.: Diagnosing thyroid disease by neural networks. Biomed. Pharmacol. J. 10(02), 509–524 (2017)

    Article  Google Scholar 

  42. Geetha, K., Santhosh Baboo, S.: Efficient thyroid disease classification using differential evolution with SVM. Indian J. Sci. Develop. Res. 88(3), 110 (2016)

    Google Scholar 

  43. Shankar, K., Lakshmanaprabu, S.K., Gupta, Deepak., Maseleno, Andino, de Albuquerque, Victor Hugo C.: Optimal feature-based multi-kernel SVM approach for thyroid disease classification. J. Supercomput. 76(2), 1128–1143 (2018). https://doi.org/10.1007/s11227-018-2469-4

    Article  Google Scholar 

  44. Sidiq, U., Aaqib, S.M., Khan, R.A.: Diagnosis of various thyroid ailments using data mining classification techniques. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 5, 131–136 (2019)

    Google Scholar 

  45. Tyagi, A., Mehra, R., Saxena, A.: Interactive thyroid disease prediction system using machine learning technique. In: 5th International Conference on Parallel, Distributed and Grid Computing (2018)

    Google Scholar 

  46. Dash, S., Das, M.N., Mishra, B.K.: Implementation of an optimized classification model for prediction of hypothyroid disease risks. In: International Conference on Inventive Computation Technologies (2016)

    Google Scholar 

  47. Pandey, S., Miri, R., Tandan, S.R.: Diagnosis and classification of hypothyroid disease using data mining techniques. Indian J. Eng. Res. Technol. 2 (2013)

    Google Scholar 

  48. Saiti, F., Naini, A.A., Shoorehdeli, M.A., Teshnehlab, M.: Thyroid disease diagnosis based on genetic algorithms using PNN and SVM. In: 3rd International Conference on Bioinformatics and Biomedical Engineering (2009)

    Google Scholar 

  49. Ma, L., Ma, C., Liu, Y., Wang, X.: Thyroid diagnosis from SPECT images using convolutional neural network with optimization. Comput. Intell. Neurosci. 2019, 1–11 (2019)

    Google Scholar 

  50. Razia, S., Rao, M.R.N.: Thyroid disorder detection using image segmentation in medical images. Indian J. Sci. Develop. Res. (2016)

    Google Scholar 

  51. Gomathy, V., Snekhalatha, U.: Automated segmentation using PCA and area estimation of thyroid gland using ultrasound images. In: 2015 International Conference on Innovations in Information, Embedded and Communication Systems (2015)

    Google Scholar 

  52. Wang, W., Ozolek, J.A., Rohde, G.K.: Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images. Cytometry Part A 9999A. (2010)

    Google Scholar 

  53. Yamamoto, S., Ogawa-Ochiai, K., Nakaguchi, T., Tsumura, N., Namiki, T., Miyake, Y.: Detecting hyper-/hypothyroidism from tongue color spectrum. In: 10th International Workshop on Biomedical Engineering (2011)

    Google Scholar 

  54. Vaz, V.A.S.: Diagnosis of hypo and hyperthyroid using MLPN network. Indian J. Innov. Res. Sci. Eng. Technol. 3(7), 14314–14323 (2014)

    Google Scholar 

  55. Mahajan, P., Madhe, S.: Hypo and hyperthyroid disorder detection from thermal images using Bayesian Classifier. In: 2014 International Conference on Advances in Communication and Computing Technologies (2014)

    Google Scholar 

  56. Zabidi, A., Khuan, L.Y., Mansor,W., Yassin, I.M., Sahak, R.: Binary particle swarm optimization for feature selection in detection of infants with hypothyroidism. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Surat, India

    Bhavisha S. Parmar & Mayuri A. Mehta

Authors
  1. Bhavisha S. Parmar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mayuri A. Mehta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bhavisha S. Parmar .

Editor information

Editors and Affiliations

  1. ISAE-ENSMA, Chasseneuil, France

    Ladjel Bellatreche

  2. Indraprastha Institute of Information Technology, New Delhi, India

    Vikram Goyal

  3. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  4. Ashoka University, Sonepat, India

    Anirban Mondal

  5. IIIT Hyderabad, Hyderabad, India

    P. Krishna Reddy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Parmar, B.S., Mehta, M.A. (2020). Computer-Aided Diagnosis of Thyroid Dysfunction: A Survey. In: Bellatreche, L., Goyal, V., Fujita, H., Mondal, A., Reddy, P.K. (eds) Big Data Analytics. BDA 2020. Lecture Notes in Computer Science(), vol 12581. Springer, Cham. https://doi.org/10.1007/978-3-030-66665-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-66665-1_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66664-4

  • Online ISBN: 978-3-030-66665-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation