Detecting six different types of Thyroid Diseases using Deep Learning approaches

Ananthi, N.; Kumaran, K.; Duraiarumugam, S.S.L.; Lavanya, P.M.; Ganesan, Sharanya; Sathiya, Girinathan R; Surya, J

doi:10.1109/accai53970.2022.9752581

Cited by 7 publications

(1 citation statement)

References 4 publications

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“… Ananthi et al (2022) and Khan (2021) exploit convolutional neural networks to try to detect the onset of thyroid dysfunctions. The former employs X-ray images to prevent hypo- and hyperthyroidism as well as malignant nodules and other dysfunctions reaching a 99% of accurate prediction.…”

Section: Resultsmentioning

confidence: 99%

A systematic review on artificial intelligence techniques for detecting thyroid diseases

Aversano,

Bernardi,

Cimitile

et al. 2023

PeerJ Computer Science

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The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years. In this context, early detection of diseases is the most common area of application. In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discovered quite early. Detecting thyroid diseases is crucial in order to treat patients effectively and promptly, by saving lives and reducing healthcare costs. This work aims at systematically reviewing and analyzing the literature on various artificial intelligence-related techniques applied to the detection and identification of various diseases related to the thyroid gland. The contributions we reviewed are classified according to different viewpoints and taxonomies in order to highlight pros and cons of the most recent research in the field. After a careful selection process, we selected and reviewed 72 papers, analyzing them according to three main research questions, i.e., which diseases of the thyroid gland are detected by different artificial intelligence techniques, which datasets are used to perform the aforementioned detection, and what types of data are used to perform the detection. The review demonstrates that the majority of the considered papers deal with supervised methods to detect hypo- and hyperthyroidism. The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common. Finally, we discuss the outcomes of the systematic review, pointing out advantages, disadvantages, and future developments in the application of artificial intelligence for thyroid diseases detection.

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Section: Resultsmentioning

confidence: 99%