{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:47:15Z","timestamp":1781884035179,"version":"3.54.5"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,8,27]],"date-time":"2023-08-27T00:00:00Z","timestamp":1693094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is to improve the diagnosis accuracy of thyroid abnormality detection models that can be utilized to alleviate undue pressure on healthcare professionals. In this research, we proposed deep learning, metaheuristics, and a MCDM algorithms-based framework to detect thyroid-related abnormalities from ultrasound and histopathological images. The proposed method uses three recently developed deep learning techniques (DeiT, Swin Transformer, and Mixer-MLP) to extract features from the thyroid image datasets. The feature extraction techniques are based on the Image Transformer and MLP models. There is a large number of redundant features that can overfit the classifiers and reduce the generalization capabilities of the classifiers. In order to avoid the overfitting problem, six feature transformation techniques (PCA, TSVD, FastICA, ISOMAP, LLE, and UMP) are analyzed to reduce the dimensionality of the data. There are five different classifiers (LR, NB, SVC, KNN, and RF) evaluated using the 5-fold stratified cross-validation technique on the transformed dataset. Both datasets exhibit large class imbalances and hence, the stratified cross-validation technique is used to evaluate the performance. The MEREC-TOPSIS MCDM technique is used for ranking the evaluated models at different analysis stages. In the first stage, the best feature extraction and classification techniques are chosen, whereas, in the second stage, the best dimensionality reduction method is evaluated in wrapper feature selection mode. Two best-ranked models are further selected for the weighted average ensemble learning and features selection using the recently proposed meta-heuristics FOX-optimization algorithm. The PCA+FOX optimization-based feature selection + random forest model achieved the highest TOPSIS score and performed exceptionally well with an accuracy of 99.13%, F2-score of 98.82%, and AUC-ROC score of 99.13% on the ultrasound dataset. Similarly, the model achieved an accuracy score of 90.65%, an F2-score of 92.01%, and an AUC-ROC score of 95.48% on the histopathological dataset. This study exploits the combination novelty of different algorithms in order to improve the thyroid cancer diagnosis capabilities. This proposed framework outperforms the current state-of-the-art diagnostic methods for thyroid-related abnormalities in ultrasound and histopathological datasets and can significantly aid medical professionals by reducing the excessive burden on the medical fraternity.<\/jats:p>","DOI":"10.3390\/jimaging9090173","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T02:23:25Z","timestamp":1693189405000},"page":"173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4489-1621","authenticated-orcid":false,"given":"Rohit","family":"Sharma","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur 713209, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7374-3831","authenticated-orcid":false,"given":"Gautam Kumar","family":"Mahanti","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur 713209, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3555-5685","authenticated-orcid":false,"given":"Ganapati","family":"Panda","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, C.V. Raman Global University, Bhubaneswar 752054, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4079-1914","authenticated-orcid":false,"given":"Adyasha","family":"Rath","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar 752054, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2649-7652","authenticated-orcid":false,"given":"Sujata","family":"Dash","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Nagaland University, Dimapur 797112, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4107-6784","authenticated-orcid":false,"given":"Saurav","family":"Mallik","sequence":"additional","affiliation":[{"name":"Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA 02115, USA"},{"name":"Department of Pharmacology & Toxicology, The University of Arizona, Tucson, MA 85721, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5549-3082","authenticated-orcid":false,"given":"Ruifeng","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Neurology, Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3042","DOI":"10.1200\/JCO.19.01411","article-title":"Role of patient maximizing-minimizing preferences in thyroid cancer surveillance","volume":"37","author":"Evron","year":"2019","journal-title":"J. Clin. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","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":"Comput. Sci. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1016\/S0272-2712(18)30435-9","article-title":"Thyroid imaging techniques","volume":"13","author":"Reading","year":"1993","journal-title":"Clin. Lab. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e1474","DOI":"10.1002\/widm.1474","article-title":"A survey on artificial intelligence in histopathology image analysis","volume":"12","author":"Abdelsamea","year":"2022","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xu, P., Du, Z., Sun, L., Zhang, Y., Zhang, J., and Qiu, Q. (2022). Diagnostic Value of Contrast-Enhanced Ultrasound Image Features under Deep Learning in Benign and Malignant Thyroid Lesions. Sci. Program., 2022.","DOI":"10.1155\/2022\/6786966"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"27917","DOI":"10.1109\/ACCESS.2022.3156096","article-title":"Automatic Thyroid Ultrasound Image Classification Using Feature Fusion Network","volume":"10","author":"Zhao","year":"2022","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rehman, H.A.U., Lin, C.Y., and Su, S.F. (2021). Deep learning based fast screening approach on ultrasound images for thyroid nodules diagnosis. Diagnostics, 11.","DOI":"10.3390\/diagnostics11122209"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1763803","DOI":"10.1155\/2020\/1763803","article-title":"Convolutional neural network for breast and thyroid nodules diagnosis in ultrasound imaging","volume":"2020","author":"Liang","year":"2020","journal-title":"BioMed Res. Int."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"101555","DOI":"10.1016\/j.media.2019.101555","article-title":"Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks","volume":"58","author":"Liu","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s10278-017-9997-y","article-title":"Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network","volume":"30","author":"Chi","year":"2017","journal-title":"J. Digit. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sai Sundar, K., Rajamani, K.T., and Siva Sankara Sai, S. (2018, January 16\u201317). Exploring Image Classification of Thyroid Ultrasound Images Using Deep Learning. Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB), Palladam, India.","DOI":"10.1007\/978-3-030-00665-5_151"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., Pham, T.D., Batchuluun, G., Yoon, H.S., and Park, K.R. (2019). Artificial intelligence-based thyroid nodule classification using information from spatial and frequency domains. J. Clin. Med., 8.","DOI":"10.3390\/jcm8111976"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., Kang, J.K., Pham, T.D., Batchuluun, G., and Park, K.R. (2020). Ultrasound image-based diagnosis of malignant thyroid nodule using artificial intelligence. Sensors, 20.","DOI":"10.3390\/s20071822"},{"key":"ref_14","first-page":"18","article-title":"Thyroid Nodules Classification using Weighted Average Ensemble and D-CRITIC based TOPSIS Methods for Ultrasound Images","volume":"20","author":"Sharma","year":"2023","journal-title":"Curr. Med. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100309","DOI":"10.1016\/j.jpi.2023.100309","article-title":"Current status of machine learning in thyroid cytopathology","volume":"14","author":"Wong","year":"2023","journal-title":"J. Pathol. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.mpdhp.2023.06.013","article-title":"The minefield of indeterminate thyroid nodules: Could artificial intelligence be a suitable diagnostic tool?","volume":"29","author":"Fiorentino","year":"2023","journal-title":"Diagn. Histopathol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1002\/cncy.22669","article-title":"Application of deep learning as an ancillary diagnostic tool for thyroid FNA cytology","volume":"131","author":"Hirokawa","year":"2023","journal-title":"Cancer Cytopathol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1159\/000512097","article-title":"Artificial intelligence in thyroid fine needle aspiration biopsies","volume":"65","author":"Kezlarian","year":"2021","journal-title":"Acta Cytol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.31557\/APJCP.2023.24.4.1379","article-title":"Artificial intelligence role in subclassifying cytology of thyroid follicular neoplasm","volume":"24","author":"Alabrak","year":"2023","journal-title":"Asian Pac. J. Cancer Prev. APJCP"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1111\/cyt.12828","article-title":"Impact of image analysis and artificial intelligence in thyroid pathology, with particular reference to cytological aspects","volume":"31","author":"Girolami","year":"2020","journal-title":"Cytopathology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"468","DOI":"10.21037\/atm.2019.08.54","article-title":"Using deep convolutional neural networks for multi-classification of thyroid tumor by histopathology: A large-scale pilot study","volume":"7","author":"Wang","year":"2019","journal-title":"Ann. Transl. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"145216","DOI":"10.1109\/ACCESS.2020.3014863","article-title":"Decision support system for classification medullary thyroid cancer","volume":"8","author":"Chandio","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"88429","DOI":"10.1109\/ACCESS.2021.3076158","article-title":"Classification of thyroid carcinoma in whole slide images using cascaded CNN","volume":"9","author":"Hassan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Do, T.H., and Khanh, H.N. (2022, January 13\u201314). Supporting Thyroid Cancer Diagnosis based on Cell Classification over Microscopic Images. Proceedings of the 2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), Phu Quoc, Vietnam.","DOI":"10.1109\/MAPR56351.2022.9924821"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"B\u00f6hland, M., Tharun, L., Scherr, T., Mikut, R., Hagenmeyer, V., Thompson, L.D., Perner, S., and Reischl, M. (2021). Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0257635"},{"key":"ref_26","first-page":"24261","article-title":"Mlp-mixer: An all-mlp architecture for vision","volume":"34","author":"Tolstikhin","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_28","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and J\u00e9gou, H. (2021, January 18\u201324). Training data-efficient image transformers & distillation through attention. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1109\/TVCG.2019.2944182","article-title":"Toward a quantitative survey of dimension reduction techniques","volume":"27","author":"Espadoto","year":"2019","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"122832","DOI":"10.1109\/ACCESS.2022.3221194","article-title":"A Meta-Analysis Survey on the Usage of Meta-Heuristic Algorithms for Feature Selection on High-Dimensional Datasets","volume":"10","author":"Yab","year":"2022","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e1249","DOI":"10.1002\/widm.1249","article-title":"Ensemble learning: A survey","volume":"8","author":"Sagi","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1007\/s00521-021-06481-x","article-title":"A weighted ensemble classifier based on WOA for classification of diabetes","volume":"34","author":"Khademi","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1007\/s10489-022-03533-0","article-title":"FOX: A FOX-inspired optimization algorithm","volume":"53","author":"Mohammed","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"99115","DOI":"10.1109\/ACCESS.2020.2995597","article-title":"Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methods","volume":"8","author":"Mohammed","year":"2020","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Nguyen, H.Q., Nguyen, V.T., Phan, D.P., Tran, Q.H., and Vu, N.P. (2022). Multi-criteria decision making in the PMEDM process by using MARCOS, TOPSIS, and MAIRCA methods. Appl. Sci., 12.","DOI":"10.3390\/app12083720"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pedraza, L., Vargas, C., Narv\u00e1ez, F., Dur\u00e1n, O., Mu\u00f1oz, E., and Romero, E. (2015, January 14\u201316). An open access thyroid ultrasound image database. Proceedings of the 10th International Symposium on Medical Information Processing and Analysis, SPIE, Cartagena de Indias, Colombia.","DOI":"10.1117\/12.2073532"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1007\/s12022-018-9520-0","article-title":"An international interobserver variability reporting of the nuclear scoring criteria to diagnose noninvasive follicular thyroid neoplasm with papillary-like nuclear features: A validation study","volume":"29","author":"Thompson","year":"2018","journal-title":"Endocr. Pathol."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/9\/173\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:40:22Z","timestamp":1760128822000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/9\/173"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,27]]},"references-count":37,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["jimaging9090173"],"URL":"https:\/\/doi.org\/10.3390\/jimaging9090173","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,27]]}}}