{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:42:33Z","timestamp":1777891353031,"version":"3.51.4"},"reference-count":28,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["THC"],"published-print":{"date-parts":[[2024,11,8]]},"abstract":"<jats:p>BACKGROUND: Early pregnancy thyroid function assessment in mothers is covered. The benefits of using load-specific reference ranges are well-established. OBJECTIVE: We pondered whether the categorization of maternal thyroid function would change if multiple blood samples obtained early in pregnancy were used. Even though binary classification is a common goal of current disease diagnosis techniques, the data sets are small, and the outcomes are not validated. Most current approaches concentrate on model optimization, focusing less on feature engineering. METHODS: The suggested method can predict increased protein binding, non-thyroid syndrome (NTIS) (simultaneous non-thyroid disease), autoimmune thyroiditis (compensated hypothyroidism), and Hashimoto\u2019s thyroiditis (primary hypothyroidism). In this paper, we develop an automatic thyroid nodule classification system using a multi-scale vision transformer and image enhancement. Graph equalization is the chosen technique for image enhancement, and in our experiments, we used neural networks with four-layer network nodes. This work presents an enhanced linguistic coverage neuro-fuzzy classifier with chosen features for thyroid disease feature selection diagnosis. The training procedure is optimized, and a multi-scale vision transformer network is employed. Each hop connection in Dense Net now has trainable weight parameters, altering the architecture. Images of thyroid nodules from 508 patients make up the data set for this article. Sets of 80% training and 20% validation and 70% training and 30% validation are created from the data. Simultaneously, we take into account how the number of training iterations, network structure, activation function of network nodes, and other factors affect the classification outcomes. RESULTS: According to the experimental results, the best number of training iterations is 500, the logistic function is the best activation function, and the ideal network structure is 2500-40-2-1. CONCLUSION: K-fold validation and performance comparison with previous research validate the suggested methodology\u2019s enhanced effectiveness.<\/jats:p>","DOI":"10.3233\/thc-240362","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T11:02:44Z","timestamp":1721991764000},"page":"4381-4402","source":"Crossref","is-referenced-by-count":5,"title":["A novel maternal thyroid disease prediction using multi-scale vision transformer architecture with improved linguistic hedges neural-fuzzy classifier"],"prefix":"10.1177","volume":"32","author":[{"given":"Summia Parveen","family":"H","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karthik","family":"S","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, SNS College of Technology, Coimbatore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sabitha","family":"R","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, SNS College of Technology, Coimbatore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/THC-240362_ref1","doi-asserted-by":"crossref","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."},{"issue":"1","key":"10.3233\/THC-240362_ref2","doi-asserted-by":"crossref","first-page":"100816","DOI":"10.1016\/j.tvir.2022.100816","article-title":"Diagnosis and management of nodular thyroid disease","volume":"25","author":"Senashova","year":"2022","journal-title":"Techniques in Vascular and Interventional Radiology."},{"key":"10.3233\/THC-240362_ref3","doi-asserted-by":"crossref","first-page":"106823","DOI":"10.1016\/j.cmpb.2022.106823","article-title":"Deep convolutional neural networks in thyroid disease detection: A multi-classification comparison by ultrasonography and computed tomography","volume":"220","author":"Zhang","year":"2022","journal-title":"Computer Methods and Programs in Biomedicine."},{"key":"10.3233\/THC-240362_ref4","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.jss.2021.09.022","article-title":"The clinical utility of preoperative thyroglobulin for surgical decision making in thyroid disease","volume":"270","author":"Kang","year":"2022","journal-title":"Journal of Surgical Research."},{"issue":"1","key":"10.3233\/THC-240362_ref5","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.fertnstert.2022.03.018","article-title":"Maternal thyroid disease in pregnancy and timing of pubertal development in sons and daughters","volume":"118","author":"Lunddorf","year":"2022","journal-title":"Fertility and Sterility."},{"key":"10.3233\/THC-240362_ref6","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.neucom.2021.07.024","article-title":"Automatic classification method of thyroid pathological images using multiple magnification factors","volume":"460","author":"Han","year":"2021","journal-title":"Neurocomputing."},{"key":"10.3233\/THC-240362_ref7","doi-asserted-by":"crossref","first-page":"104064","DOI":"10.1016\/j.engappai.2020.104064","article-title":"Integrate domain knowledge in training multi-task cascade deep learning model for benign\u2013malignant thyroid nodule classification on ultrasound images","volume":"98","author":"Yang","year":"2021","journal-title":"Engineering Applications of Artificial Intelligence."},{"key":"10.3233\/THC-240362_ref8","doi-asserted-by":"crossref","unstructured":"Sathyaprakash P, Alagarsundaram P, Devarajan MV, Alkhayyat A, Poovendran P, Rani DR, Savitha V. 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