{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T18:45:15Z","timestamp":1794077115270,"version":"4.8.0"},"reference-count":96,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T00:00:00Z","timestamp":1686009600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>\n                    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,\n                    <jats:italic>i.e.<\/jats:italic>\n                    , 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 The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common.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.\n                  <\/jats:p>","DOI":"10.7717\/peerj-cs.1394","type":"journal-article","created":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T13:41:47Z","timestamp":1686058907000},"page":"e1394","source":"Crossref","is-referenced-by-count":41,"title":["A systematic review on artificial intelligence techniques for detecting thyroid diseases"],"prefix":"10.7717","volume":"9","author":[{"given":"Lerina","family":"Aversano","sequence":"first","affiliation":[{"name":"Department of Engineering, University of Sannio, Benevento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario Luca","family":"Bernardi","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Sannio, Benevento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Cimitile","sequence":"additional","affiliation":[{"name":"Dept. of Law and Digital Society, UnitelmaSapienza University, Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Maiellaro","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Sannio, Benevento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riccardo","family":"Pecori","sequence":"additional","affiliation":[{"name":"Institute of Materials for Electronics and Magnetism, National Research Council, Parma, Italy"},{"name":"SMARTEST Research Centre, eCampus University, Novedrate (CO), Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2023,6,6]]},"reference":[{"key":"10.7717\/peerj-cs.1394\/ref-1","article-title":"A systematic review on the role of artificial intelligence in sonographic diagnosis of thyroid cancer: past, present and future","author":"Abdolali","year":"2020","journal-title":"CoRR"},{"key":"10.7717\/peerj-cs.1394\/ref-2","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1109\/ICAAIC53929.2022.9792854","article-title":"Thyroid cancer detection using deep neural network","author":"Ahmed","year":"2022"},{"key":"10.7717\/peerj-cs.1394\/ref-3","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/INTELSE.2016.7475160","article-title":"TDTD: thyroid disease type diagnostics","author":"Ahmed","year":"2016"},{"issue":"3","key":"10.7717\/peerj-cs.1394\/ref-4","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1007\/s40747-021-00347-4","article-title":"ResCaps: an improved capsule network and its application in ultrasonic image classification of thyroid papillary carcinoma","volume":"8","author":"Ai","year":"2022","journal-title":"Complex & Intelligent Systems"},{"key":"10.7717\/peerj-cs.1394\/ref-5","doi-asserted-by":"publisher","first-page":"8675","DOI":"10.1007\/s00521-019-04359-7","article-title":"A dynamic ensemble learning algorithm for neural networks","volume":"32","author":"Alam","year":"2020","journal-title":"Neural Computing and Applications"},{"key":"10.7717\/peerj-cs.1394\/ref-6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCAI53970.2022.9752581","article-title":"Detecting six different types of thyroid diseases using deep learning approaches","author":"Ananthi","year":"2022"},{"issue":"4","key":"10.7717\/peerj-cs.1394\/ref-7","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1016\/j.cegh.2018.11.007","article-title":"Diagnosis of hypothyroidism using a fuzzy rule-based expert system","volume":"7","author":"Asaad Sajadi","year":"2019","journal-title":"Clinical Epidemiology and Global Health"},{"key":"10.7717\/peerj-cs.1394\/ref-8","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1016\/j.procs.2021.08.106","article-title":"Thyroid disease treatment prediction with machine learning approaches","volume":"192","author":"Aversano","year":"2021","journal-title":"Procedia Computer Science"},{"key":"10.7717\/peerj-cs.1394\/ref-9","doi-asserted-by":"publisher","first-page":"108135","DOI":"10.1016\/j.patcog.2021.108135","article-title":"Deep neural networks ensemble to detect COVID-19 from CT scans","volume":"120","author":"Aversano","year":"2021","journal-title":"Pattern Recognition"},{"key":"10.7717\/peerj-cs.1394\/ref-10","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.eswa.2018.01.025","article-title":"Amended fused TOPSIS-VIKOR for classification (ATOVIC) applied to some UCI data sets","volume":"99","author":"Baccour","year":"2018","journal-title":"Expert Systems with Applications"},{"key":"10.7717\/peerj-cs.1394\/ref-11","doi-asserted-by":"publisher","first-page":"113335","DOI":"10.1016\/j.eswa.2020.113335","article-title":"A case-based reasoning system for supervised classification problems in the medical field","volume":"150","author":"Bentaiba-Lagrid","year":"2020","journal-title":"Expert Systems with Applications"},{"issue":"19","key":"10.7717\/peerj-cs.1394\/ref-12","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13194740","article-title":"Artificial intelligence in thyroid field\u2014a comprehensive review","volume":"13","author":"Bini","year":"2021","journal-title":"Cancer"},{"key":"10.7717\/peerj-cs.1394\/ref-13","doi-asserted-by":"publisher","first-page":"149787","DOI":"10.1109\/ACCESS.2020.3016676","article-title":"Diagnosis method of thyroid disease combining knowledge graph and deep learning","volume":"8","author":"Chai","year":"2020","journal-title":"IEEE Access"},{"key":"10.7717\/peerj-cs.1394\/ref-14","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/s40012-016-0100-5","article-title":"A comparative study on thyroid disease detection using K-nearest neighbor and Naive Bayes classification techniques","volume":"4","author":"Chandel","year":"2016","journal-title":"CSI Transactions on ICT"},{"key":"10.7717\/peerj-cs.1394\/ref-15","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1109\/ICECUBE.2016.7495206","article-title":"TDV: intelligent system for thyroid disease visualization","author":"Chandio","year":"2016"},{"issue":"3","key":"10.7717\/peerj-cs.1394\/ref-16","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s40009-020-00979-z","article-title":"Thyroid disease prediction using machine learning approaches","volume":"44","author":"Chaubey","year":"2021","journal-title":"National Academy Science Letters"},{"key":"10.7717\/peerj-cs.1394\/ref-17","doi-asserted-by":"publisher","first-page":"105329","DOI":"10.1016\/j.cmpb.2020.105329","article-title":"A review of thyroid gland segmentation and thyroid nodule segmentation methods for medical ultrasound images","volume":"185","author":"Chen","year":"2020","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"10.7717\/peerj-cs.1394\/ref-18","doi-asserted-by":"publisher","first-page":"105906","DOI":"10.1016\/j.cmpb.2020.105906","article-title":"Ultrasonic thyroid nodule detection method based on U-Net network","volume":"199","author":"Chu","year":"2021","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"1","key":"10.7717\/peerj-cs.1394\/ref-19","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s13044-021-00107-z","article-title":"Advanced thyroid carcinomas: neural network analysis of ultrasonographic characteristics","volume":"14","author":"Cordes","year":"2021","journal-title":"Thyroid Research"},{"key":"10.7717\/peerj-cs.1394\/ref-20","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1109\/Confluence47617.2020.9058102","article-title":"Prediction of thyroid disorders using advanced machine learning techniques","author":"Duggal","year":"2020"},{"key":"10.7717\/peerj-cs.1394\/ref-21","doi-asserted-by":"publisher","first-page":"100370","DOI":"10.1016\/j.cosrev.2021.100370","article-title":"Role of machine learning in medical research: a survey","volume":"40","author":"Garg","year":"2021","journal-title":"Computer Science Review"},{"key":"10.7717\/peerj-cs.1394\/ref-22","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1109\/ICASID.2019.8925267","article-title":"Classification of thyroid ultrasound standard plane images using ResNet-18 networks","author":"Guo","year":"2019"},{"key":"10.7717\/peerj-cs.1394\/ref-23","first-page":"452","article-title":"Prognosis of thyroid disease using MS-apriori improved decision tree","author":"Hao","year":"2018"},{"key":"10.7717\/peerj-cs.1394\/ref-24","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.knosys.2017.06.011","article-title":"Synergy effects between grafting and subdivision in Re-RX with J48graft for the diagnosis of thyroid disease","volume":"131","author":"Hayashi","year":"2017","journal-title":"Knowledge-Based Systems"},{"issue":"4","key":"10.7717\/peerj-cs.1394\/ref-25","doi-asserted-by":"publisher","first-page":"3616","DOI":"10.1007\/s11227-020-03404-w","article-title":"A multiple multilayer perceptron neural network with an adaptive learning algorithm for thyroid disease diagnosis in the internet of medical things","volume":"77","author":"Hosseinzadeh","year":"2021","journal-title":"The Journal of Supercomputing"},{"key":"10.7717\/peerj-cs.1394\/ref-26","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.eswa.2016.07.041","article-title":"Dimensionality reduction in data mining: a copula approach","volume":"64","author":"Houari","year":"2016","journal-title":"Expert Systems with Applications"},{"key":"10.7717\/peerj-cs.1394\/ref-27","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.jss.2017.05.117","article-title":"Machine learning to identify multigland disease in primary hyperparathyroidism","volume":"219","author":"Imbus","year":"2017","journal-title":"Journal of Surgical Research"},{"key":"10.7717\/peerj-cs.1394\/ref-28","doi-asserted-by":"publisher","first-page":"e898","DOI":"10.7717\/peerj-cs.898","article-title":"Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study","volume":"8","author":"Islam","year":"2022","journal-title":"PeerJ Computer Science"},{"key":"10.7717\/peerj-cs.1394\/ref-29","first-page":"205","article-title":"Decision stump and stackingc-based hybrid algorithm for healthcare data classification","author":"Jha","year":"2018"},{"key":"10.7717\/peerj-cs.1394\/ref-30","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.clinimag.2020.12.012","article-title":"Diagnosis of thyroid cancer using a TI-RADS-based computer-aided diagnosis system: a multicenter retrospective study","volume":"80","author":"Jin","year":"2021","journal-title":"Clinical Imaging"},{"issue":"2","key":"10.7717\/peerj-cs.1394\/ref-31","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1007\/s11277-022-09823-7","article-title":"Expanded and filtered features based ELM model for thyroid disease classification","volume":"126","author":"Juneja","year":"2022","journal-title":"Wireless Personal Communications"},{"issue":"14","key":"10.7717\/peerj-cs.1394\/ref-32","doi-asserted-by":"publisher","first-page":"19905","DOI":"10.1007\/s11042-019-7327-8","article-title":"A healthcare monitoring system using random forest and internet of things (IoT)","volume":"78","author":"Kaur","year":"2019","journal-title":"Multimedia Tools and Applications"},{"issue":"1","key":"10.7717\/peerj-cs.1394\/ref-33","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1093\/emph\/eoaa043","article-title":"Reinterpreting patterns of variation in human thyroid function: an evolutionary ecology perspective","volume":"9","author":"Keestra","year":"2020","journal-title":"Evolution, Medicine, and Public Health"},{"key":"10.7717\/peerj-cs.1394\/ref-34","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/Confluence51648.2021.9377097","article-title":"Application of two-class neural network-based classification model to predict the onset of thyroid disease","author":"Khan","year":"2021"},{"key":"10.7717\/peerj-cs.1394\/ref-35","doi-asserted-by":"publisher","first-page":"1615","DOI":"10.1007\/s11277-021-08708-5","article-title":"Artificial intelligence and internet of things based healthcare 4.0 monitoring system","volume":"127","author":"Kishor","year":"2021","journal-title":"Wireless Personal Communications"},{"key":"10.7717\/peerj-cs.1394\/ref-36","first-page":"1","article-title":"Procedures for performing systematic reviews","author":"Kitchenham","year":"2004"},{"issue":"7","key":"10.7717\/peerj-cs.1394\/ref-37","doi-asserted-by":"publisher","first-page":"4651","DOI":"10.1007\/s10462-020-09804-x","article-title":"Usage and implementation of neuro-fuzzy systems for classification and prediction in the diagnosis of different types of medical disorders: a decade review","volume":"53","author":"Kour","year":"2020","journal-title":"Artificial Intelligence Review"},{"key":"10.7717\/peerj-cs.1394\/ref-38","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1109\/ICSSIT48917.2020.9214180","article-title":"A novel approach of SVM based classification on thyroid disease stage detection","author":"Kumar","year":"2020"},{"key":"10.7717\/peerj-cs.1394\/ref-39","doi-asserted-by":"publisher","author":"Kumari","year":"2019","DOI":"10.1145\/3339311.3339346"},{"key":"10.7717\/peerj-cs.1394\/ref-40","article-title":"DeepHealth: deep learning for health informatics","author":"Kwak","year":"2019"},{"issue":"2","key":"10.7717\/peerj-cs.1394\/ref-41","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/S1470-2045(18)30762-9","article-title":"Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study","volume":"20","author":"Li","year":"2019","journal-title":"The Lancet Oncology"},{"key":"10.7717\/peerj-cs.1394\/ref-42","first-page":"441","article-title":"A hybrid intelligent framework for thyroid diagnosis","author":"Li","year":"2019"},{"issue":"7","key":"10.7717\/peerj-cs.1394\/ref-43","doi-asserted-by":"publisher","first-page":"5001","DOI":"10.1007\/s00330-020-07585-z","article-title":"Thyroid nodule recognition using a joint convolutional neural network with information fusion of ultrasound images and radiofrequency data","volume":"31","author":"Liu","year":"2021","journal-title":"European Radiology"},{"key":"10.7717\/peerj-cs.1394\/ref-44","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1016\/j.mpaic.2014.07.006","article-title":"Thyroid disease and thyroid surgery","volume":"15","author":"Longbottom","year":"2014","journal-title":"Anaesthesia & Intensive Care Medicine"},{"key":"10.7717\/peerj-cs.1394\/ref-45","first-page":"209","article-title":"Hyperthyroidism progress prediction with enhanced LSTM","author":"Lu","year":"2020"},{"issue":"3","key":"10.7717\/peerj-cs.1394\/ref-46","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15030708","article-title":"The use of artificial intelligence in the diagnosis and classification of thyroid nodules: an update","volume":"15","author":"Ludwig","year":"2023","journal-title":"Cancer"},{"key":"10.7717\/peerj-cs.1394\/ref-47","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1145\/3233547.3233588","article-title":"Deep learning based tumor type classification using gene expression data","author":"Lyu","year":"2018"},{"key":"10.7717\/peerj-cs.1394\/ref-48","first-page":"86","article-title":"An efficient diagnosis system for thyroid disease based on enhanced kernelized extreme learning machine approach","author":"Ma","year":"2018"},{"key":"10.7717\/peerj-cs.1394\/ref-49","doi-asserted-by":"publisher","first-page":"6212759","DOI":"10.1155\/2019\/6212759","article-title":"Thyroid diagnosis from SPECT images using convolutional neural network with optimization","volume":"2019","author":"Ma","year":"2019","journal-title":"Computational Intelligence and Neuroscience"},{"key":"10.7717\/peerj-cs.1394\/ref-50","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1109\/ICOEI.2017.8300768","article-title":"Normalization using improvised K-Means applied in diagnosing thyroid disease with ANN","author":"Mahurkar","year":"2017"},{"key":"10.7717\/peerj-cs.1394\/ref-51","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1109\/ICTS52701.2021.9608400","article-title":"Application of data mining techniques in diagnosing various thyroid ailments: a review","author":"Mendoza","year":"2021"},{"issue":"4","key":"10.7717\/peerj-cs.1394\/ref-52","doi-asserted-by":"publisher","first-page":"1428","DOI":"10.1210\/jc.2002-021260","article-title":"Classification of thyroid diseases: suggestions for a revision","volume":"88","author":"Monaco","year":"2003","journal-title":"The Journal of Clinical Endocrinology & Metabolism"},{"key":"10.7717\/peerj-cs.1394\/ref-53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/IJCNN.2018.8489032","article-title":"Identification of thyroid nodules in infrared images by convolutional neural networks","author":"Moran","year":"2018"},{"issue":"1","key":"10.7717\/peerj-cs.1394\/ref-54","doi-asserted-by":"publisher","DOI":"10.1186\/s13643-020-01459-7","article-title":"Prevalence of hyperthyroidism, hypothyroidism, and euthyroidism in thyroid eye disease: a systematic review of the literature","volume":"9","author":"Mu\u00f1oz-Ortiz","year":"2020","journal-title":"Systematic Reviews"},{"key":"10.7717\/peerj-cs.1394\/ref-55","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"10.7717\/peerj-cs.1394\/ref-56","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1109\/ITME.2016.0134","article-title":"Improved ensemble classification method of thyroid disease based on random forest","author":"Pan","year":"2016"},{"key":"10.7717\/peerj-cs.1394\/ref-57","first-page":"164","article-title":"Computer-aided diagnosis of thyroid dysfunction: a survey","author":"Parmar","year":"2020"},{"key":"10.7717\/peerj-cs.1394\/ref-58","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1109\/ICICT48043.2020.9112406","article-title":"Ensemble gain ratio feature selection (EGFS) model with machine learning and data mining algorithms for disease risk prediction","author":"Pasha","year":"2020"},{"key":"10.7717\/peerj-cs.1394\/ref-59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/IGEHT.2017.8094070","article-title":"Feature selection algorithms to improve thyroid disease diagnosis","author":"Pavya","year":"2017"},{"key":"10.7717\/peerj-cs.1394\/ref-60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICSCT53883.2021.9642544","article-title":"Predictive analysis for thyroid diseases diagnosis using machine learning","author":"Peya","year":"2021"},{"key":"10.7717\/peerj-cs.1394\/ref-61","doi-asserted-by":"publisher","first-page":"107531","DOI":"10.1016\/j.knosys.2021.107531","article-title":"Fusing deep and handcrafted features for intelligent recognition of uptake patterns on thyroid scintigraphy","volume":"236","author":"Pi","year":"2022","journal-title":"Knowledge-Based Systems"},{"key":"10.7717\/peerj-cs.1394\/ref-62","doi-asserted-by":"publisher","first-page":"5828","DOI":"10.1109\/EMBC.2019.8857929","article-title":"Patch based texture classification of thyroid ultrasound images using convolutional neural network","author":"Poudel","year":"2019"},{"key":"10.7717\/peerj-cs.1394\/ref-63","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1109\/I-SMAC.2018.8653748","article-title":"Tuning the parameters of weighted ELM using IWO and BAT algorithm to improve the classification performance","author":"Priya","year":"2018"},{"key":"10.7717\/peerj-cs.1394\/ref-64","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1109\/ICCMC53470.2022.9753986","article-title":"Efficient thyroid disease prediction using features selection and meta-classifiers","author":"Priyadharsini","year":"2022"},{"key":"10.7717\/peerj-cs.1394\/ref-65","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.compmedimag.2018.10.001","article-title":"Patch-based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition","volume":"71","author":"Prochazka","year":"2019","journal-title":"Computerized Medical Imaging and Graphics"},{"issue":"10","key":"10.7717\/peerj-cs.1394\/ref-66","doi-asserted-by":"publisher","first-page":"1312","DOI":"10.1007\/s11547-021-01393-1","article-title":"Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery","volume":"126","author":"Qin","year":"2021","journal-title":"La Radiologia Medica"},{"key":"10.7717\/peerj-cs.1394\/ref-67","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.compbiomed.2018.02.002","article-title":"Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images","volume":"95","author":"Raghavendra","year":"2018","journal-title":"Computers in Biology and Medicine"},{"key":"10.7717\/peerj-cs.1394\/ref-68","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.ultras.2017.02.003","article-title":"Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions","volume":"77","author":"Raghavendra","year":"2017","journal-title":"Ultrasonics"},{"key":"10.7717\/peerj-cs.1394\/ref-69","first-page":"140","article-title":"Thyroid prediction using machine learning techniques","author":"Raisinghani","year":"2019"},{"key":"10.7717\/peerj-cs.1394\/ref-70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/INOCON50539.2020.9298252","article-title":"A machine learning approach to predict thyroid disease at early stages of diagnosis","author":"Rao","year":"2020"},{"key":"10.7717\/peerj-cs.1394\/ref-71","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/978-3-030-38445-6_15","article-title":"Machine learning techniques for thyroid disease diagnosis: a systematic review","volume-title":"Modern approaches in machine learning and cognitive science: a walkthrough: latest trends in AI","author":"Razia","year":"2020"},{"key":"10.7717\/peerj-cs.1394\/ref-72","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/ICICT4SD50815.2021.9397052","article-title":"Prediction of thyroid disease (hypothyroid) in early stage using feature selection and classification techniques","author":"Riajuliislam","year":"2021"},{"issue":"12","key":"10.7717\/peerj-cs.1394\/ref-73","doi-asserted-by":"publisher","first-page":"17773","DOI":"10.1007\/s11042-020-10243-7","article-title":"Multi-layer tree liquid state machine recurrent auto encoder for thyroid detection","volume":"80","author":"Saktheeswari","year":"2021","journal-title":"Multimedia Tools and Applications"},{"issue":"8","key":"10.7717\/peerj-cs.1394\/ref-74","doi-asserted-by":"publisher","first-page":"2163","DOI":"10.1007\/s00216-021-03183-0","article-title":"Discrimination of malignant from benign thyroid lesions through neural networks using FTIR signals obtained from tissues","volume":"413","author":"Santillan","year":"2021","journal-title":"Analytical and Bioanalytical Chemistry"},{"key":"10.7717\/peerj-cs.1394\/ref-75","first-page":"47","article-title":"A many-valued empirical machine for thyroid dysfunction assessment","author":"Santos","year":"2019"},{"key":"10.7717\/peerj-cs.1394\/ref-76","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1109\/ICCES45898.2019.9002284","article-title":"A study on label TSH, T3, T4U, TT4, FTI in hyperthyroidism and hypothyroidism using machine learning techniques","author":"Shahid","year":"2019"},{"issue":"2","key":"10.7717\/peerj-cs.1394\/ref-77","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1016\/j.bbe.2021.02.008","article-title":"Deep learning on ultrasound images of thyroid nodules","volume":"41","author":"Sharifi","year":"2021","journal-title":"Biocybernetics and Biomedical Engineering"},{"key":"10.7717\/peerj-cs.1394\/ref-78","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.knosys.2016.01.002","article-title":"Evolving support vector machines using fruit fly optimization for medical data classification","volume":"96","author":"Shen","year":"2016","journal-title":"Knowledge-Based Systems"},{"issue":"3","key":"10.7717\/peerj-cs.1394\/ref-79","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1007\/s12020-020-02523-x","article-title":"Diagnosis of thyroid neoplasm using support vector machine algorithms based on platelet RNA-seq","volume":"72","author":"Shen","year":"2021","journal-title":"Endocrine"},{"key":"10.7717\/peerj-cs.1394\/ref-80","doi-asserted-by":"publisher","first-page":"105611","DOI":"10.1016\/j.cmpb.2020.105611","article-title":"Knowledge-guided synthetic medical image adversarial augmentation for ultrasonography thyroid nodule classification","volume":"196","author":"Shi","year":"2020","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"10.7717\/peerj-cs.1394\/ref-81","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1109\/ICCS45141.2019.9065824","article-title":"Disease diagnosis through data mining techniques","author":"Sidiq","year":"2019"},{"issue":"10","key":"10.7717\/peerj-cs.1394\/ref-82","doi-asserted-by":"publisher","first-page":"11738","DOI":"10.1007\/s10489-021-02967-2","article-title":"Dual-branch network via pseudo-label training for thyroid nodule detection in ultrasound image","volume":"52","author":"Song","year":"2022","journal-title":"Applied Intelligence"},{"issue":"14","key":"10.7717\/peerj-cs.1394\/ref-83","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14143357","article-title":"Artificial intelligence for thyroid nodule characterization: where are we standing?","volume":"14","author":"Sorrenti","year":"2022","journal-title":"Cancer"},{"key":"10.7717\/peerj-cs.1394\/ref-84","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1109\/PDGC.2018.8745910","article-title":"Interactive thyroid disease prediction system using machine learning technique","author":"Tyagi","year":"2018"},{"key":"10.7717\/peerj-cs.1394\/ref-85","first-page":"572","article-title":"Peri-diagnostic decision support through cost-efficient feature acquisition at test-time","author":"Vivar","year":"2020"},{"key":"10.7717\/peerj-cs.1394\/ref-86","volume-title":"Data mining, practical machine learning tools","author":"Witten","year":"2011"},{"key":"10.7717\/peerj-cs.1394\/ref-87","doi-asserted-by":"publisher","first-page":"9492056","DOI":"10.1155\/2022\/9492056","article-title":"Accuracy of ultrasound diagnosis of thyroid nodules based on artificial intelligence-assisted diagnostic technology: a systematic review and meta-analysis","volume":"2022","author":"Xue","year":"2022","journal-title":"International Journal of Endocrinology"},{"issue":"3","key":"10.7717\/peerj-cs.1394\/ref-88","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1007\/s41870-019-00395-7","article-title":"Thyroid prediction using ensemble data mining techniques","volume":"14","author":"Yadav","year":"2022","journal-title":"International Journal of Information Technology"},{"issue":"1","key":"10.7717\/peerj-cs.1394\/ref-89","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1186\/s12880-021-00710-4","article-title":"Automatic differentiation of thyroid scintigram by deep convolutional neural network: a dual center study","volume":"21","author":"Yang","year":"2021","journal-title":"BMC Medical Imaging"},{"key":"10.7717\/peerj-cs.1394\/ref-90","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1109\/ICVRV.2017.00038","article-title":"A novel intelligent thyroid nodule diagnosis system over ultrasound images based on deep learning","author":"Yi","year":"2017"},{"key":"10.7717\/peerj-cs.1394\/ref-91","doi-asserted-by":"publisher","first-page":"1792","DOI":"10.1109\/ICCC47050.2019.9064066","article-title":"Hybrid regional feature cutting network for thyroid ultrasound images classification","author":"Yin","year":"2019"},{"key":"10.7717\/peerj-cs.1394\/ref-92","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2212.00173","article-title":"SPADE: semi-supervised anomaly detection under distribution mismatch, arXiv","author":"Yoon","year":"2022"},{"key":"10.7717\/peerj-cs.1394\/ref-93","doi-asserted-by":"publisher","first-page":"101923","DOI":"10.1016\/j.pdpdt.2020.101923","article-title":"Fourier transform infrared spectroscopy combined with deep learning and data enhancement for quick diagnosis of abnormal thyroid function","volume":"32","author":"Yue","year":"2020","journal-title":"Photodiagnosis and Photodynamic Therapy"},{"key":"10.7717\/peerj-cs.1394\/ref-94","doi-asserted-by":"publisher","first-page":"100281","DOI":"10.1016\/j.imu.2019.100281","article-title":"Applying computational classification methods to diagnose Congenital Hypothyroidism: a comparative study","volume":"18","author":"Zarin Mousavi","year":"2020","journal-title":"Informatics in Medicine Unlocked"},{"key":"10.7717\/peerj-cs.1394\/ref-95","doi-asserted-by":"publisher","DOI":"10.1145\/3424978.3425118","article-title":"Medical image synthetic data augmentation using GAN","author":"Zhang","year":"2020"},{"issue":"9","key":"10.7717\/peerj-cs.1394\/ref-96","doi-asserted-by":"publisher","first-page":"10369","DOI":"10.1007\/s10489-021-03025-7","article-title":"Semantic consistency generative adversarial network for cross-modality domain adaptation in ultrasound thyroid nodule classification","volume":"52","author":"Zhao","year":"2022","journal-title":"Applied Intelligence"}],"container-title":["PeerJ Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/peerj.com\/articles\/cs-1394.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-1394.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-1394.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-1394.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T13:41:56Z","timestamp":1686058916000},"score":1,"resource":{"primary":{"URL":"https:\/\/peerj.com\/articles\/cs-1394"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,6]]},"references-count":96,"alternative-id":["10.7717\/peerj-cs.1394"],"URL":"https:\/\/doi.org\/10.7717\/peerj-cs.1394","archive":["CLOCKSS","LOCKSS","Portico"],"relation":{"has-review":[{"id-type":"doi","id":"10.7287\/peerj-cs.1394v0.2\/reviews\/2","asserted-by":"object"},{"id-type":"doi","id":"10.7287\/peerj-cs.1394v0.1\/reviews\/4","asserted-by":"object"},{"id-type":"doi","id":"10.7287\/peerj-cs.1394v0.1\/reviews\/3","asserted-by":"object"},{"id-type":"doi","id":"10.7287\/peerj-cs.1394v0.1\/reviews\/2","asserted-by":"object"},{"id-type":"doi","id":"10.7287\/peerj-cs.1394v0.1\/reviews\/1","asserted-by":"object"}]},"ISSN":["2376-5992"],"issn-type":[{"value":"2376-5992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,6]]},"article-number":"e1394"}}