{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:46:16Z","timestamp":1785422776597,"version":"3.56.0"},"reference-count":113,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2014Brazil","doi-asserted-by":"publisher","award":["Finance Code 001"],"award-info":[{"award-number":["Finance Code 001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"award":["Finance Code 001"],"award-info":[{"award-number":["Finance Code 001"]}],"id":[{"id":"https:\/\/ror.org\/00x0ma614","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study reports the development of a portable multispectral optoelectronic system for automated thyroid cancer detection in immunohistochemically stained histological slides. The platform integrates a 14-band AS7343 multispectral sensor, a dual-fiber optical setup operating in transreflectance geometry, and a two-dimensional scanning subsystem for spatially resolved acquisition. Data acquisition and management were implemented on a Raspberry Pi using Python, Flask, React 19.1.0, Redis, and SocketIO for control, visualization, and real-time updates. A standardized Dark\u2013White\u2013Sample protocol was adopted for baseline correction and the generation of multispectral cubes organized by spatial position and spectral band. The dataset comprised 29 patients, 84 FFPE biomarker-stained histological sections, and 66,510 original point-by-point multispectral measurements. Spectral patterns associated with malignant and non-malignant thyroid samples were analyzed using Linear Discriminant Analysis (LDA), Support Vector Machine with radial basis function kernel (SVM-RBF), and Multilayer Perceptron (MLP). All metrics were evaluated on an independent slide-level test set. LDA achieved 86.8% sensitivity, 95.9% specificity, and 91.0% accuracy. SVM-RBF and MLP achieved accuracies of 90.8% and 90.4%, respectively. Macro-averaged AUC-ROC values were 0.836, 0.865, and 0.761, respectively. These findings support the system as a portable proof-of-concept platform for computer-aided thyroid pathology.<\/jats:p>","DOI":"10.3390\/s26144448","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:55:21Z","timestamp":1783990521000},"page":"4448","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Portable Multispectral Optoelectronic System for Thyroid Cancer Detection"],"prefix":"10.3390","volume":"26","author":[{"given":"Edmilson Roberto","family":"Braga","sequence":"first","affiliation":[{"name":"Polytechnic School, Department of Electronic Systems, University of S\u00e3o Paulo (USP), S\u00e3o Paulo 05508-220, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto M\u00e1rcio","family":"Braga J\u00fanior","sequence":"additional","affiliation":[{"name":"Center for Exact Sciences, Architecture, and Engineering (CCEAE), Catholic University of Santos (UniSantos), Santos 11015-002, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2626-2132","authenticated-orcid":false,"given":"Mauro S\u00e9rgio","family":"Braga","sequence":"additional","affiliation":[{"name":"Department of Control and Automation Engineering, Federal Institute of Education, Science and Technology of S\u00e3o Paulo (IFSP), S\u00e3o Paulo 01109-010, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0156-8274","authenticated-orcid":false,"given":"Janete Maria","family":"Cerutti","sequence":"additional","affiliation":[{"name":"Genetic Bases of Thyroid Tumor Laboratory, Division of Genetics, Department of Morphology and Genetics, Federal University of S\u00e3o Paulo (UNIFESP), S\u00e3o Paulo 04023-062, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walter Jaimes","family":"Salcedo","sequence":"additional","affiliation":[{"name":"Polytechnic School, Department of Electronic Systems, University of S\u00e3o Paulo (USP), S\u00e3o Paulo 05508-220, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s12022-022-09707-3","article-title":"Overview of the 2022 WHO Classification of Thyroid Neoplasms","volume":"33","author":"Baloch","year":"2022","journal-title":"Endocr. Pathol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"703","DOI":"10.3803\/EnM.2022.1553","article-title":"Update from the 2022 World Health Organization Classification of Thyroid Tumors: A Standardized Diagnostic Approach","volume":"37","author":"Jung","year":"2022","journal-title":"Endocrinol. Metab."},{"key":"ref_3","first-page":"e220293","article-title":"The 2022 WHO classification of thyroid tumors: Novel concepts in nomenclature and grading","volume":"30","author":"Juhlin","year":"2023","journal-title":"Endocr.-Relat. Cancer"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e70449","DOI":"10.1002\/mco2.70449","article-title":"Thyroid Cancer: Pathogenesis, Clinicopathology, Diagnosis, and Management","volume":"6","author":"Li","year":"2025","journal-title":"MedComm"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Prete, A., de Souza, P.B., Censi, S., Muzza, M., Nucci, N., and Sponziello, M. (2020). Update on Fundamental Mechanisms of Thyroid Cancer. Front. Endocrinol., 11.","DOI":"10.3389\/fendo.2020.00102"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s40291-017-0303-y","article-title":"Altered Epigenetic Mechanisms in Thyroid Cancer Subtypes","volume":"22","author":"Zarkesh","year":"2018","journal-title":"Mol. Diagn. Ther."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Luz\u00f3n-Toro, B., Fern\u00e1ndez, R.M., Villalba-Benito, L., Torroglosa, A., Anti\u00f1olo, G., and Borrego, S. (2019). Influencers on Thyroid Cancer Onset: Molecular Genetic Basis. Genes, 10.","DOI":"10.3390\/genes10110913"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1172\/JCI19617","article-title":"A preoperative diagnostic test that distinguishes benign from malignant thyroid carcinoma based on gene expression","volume":"113","author":"Cerutti","year":"2024","journal-title":"J. Clin. Investig."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1158\/1078-0432.CCR-05-2226","article-title":"Diagnosis of suspicious thyroid nodules using four protein biomarkers","volume":"12","author":"Cerutti","year":"2006","journal-title":"Clin. Cancer Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"E151","DOI":"10.1210\/jc.2010-1318","article-title":"PVALB, a new H\u00fcrthle adenoma diagnostic marker identified through gene expression","volume":"96","author":"Cerutti","year":"2011","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"589","DOI":"10.2174\/138920211798120781","article-title":"Employing genetic markers to improve diagnosis of thyroid tumor fine needle biopsy","volume":"12","author":"Cerutti","year":"2011","journal-title":"Curr. Genom."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1089\/thy.2015.0020","article-title":"2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer","volume":"26","author":"Haugen","year":"2016","journal-title":"Thyroid"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2869","DOI":"10.1210\/clinem\/dgaa322","article-title":"Contemporary Thyroid Nodule Evaluation and Management","volume":"105","author":"Grani","year":"2020","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kristollari, K., Paul, A.A., Angel, S., and Marks, R.S. (2024). Conventional and Emerging Diagnostic Approaches for Differentiated Thyroid Carcinoma. Chemosensors, 12.","DOI":"10.3390\/chemosensors12110229"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"141","DOI":"10.4132\/jptm.2024.07.25.r","article-title":"Diagnostic challenges in the assessment of thyroid neoplasms using nuclear features and vascular and capsular invasion: A multi-center interobserver agreement study","volume":"59","author":"Harahap","year":"2025","journal-title":"J. Pathol. Transl. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1186\/1746-1596-7-97","article-title":"Expression of CK-19, galectin-3 and HBME-1 in the differentiation of thyroid lesions: Systematic review and diagnostic meta-analysis","volume":"7","author":"Matsubayashi","year":"2012","journal-title":"Diagn. Pathol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Crescenzi, A., and Baloch, Z.W. (2023). Immunohistochemistry in the pathologic diagnosis and management of thyroid neoplasms. Front. Endocrinol., 14.","DOI":"10.3389\/fendo.2023.1198099"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1097\/01.XEJ.0000472879.61441.9a","article-title":"Differentiation of benign from malignant follicular-cell-derived thyroid lesions: An immunohistochemical study","volume":"35","year":"2015","journal-title":"Egypt. J. Pathol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cece, A., Agresti, M., De Falco, N., Sperlongano, P., Moccia, G., Luongo, P., Miele, F., Allaria, A., Torelli, F., and Bassi, P. (2025). Role of Artificial Intelligence in Thyroid Cancer Diagnosis. J. Clin. Med., 14.","DOI":"10.3390\/jcm14072422"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6509","DOI":"10.1038\/s41598-026-37496-w","article-title":"Thyroid cancer detection and classification using spectral imaging and artificial intelligence","volume":"16","author":"Almagor","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1364\/BOE.381257","article-title":"Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning","volume":"11","author":"Halicek","year":"2020","journal-title":"Biomed. Opt. Express"},{"key":"ref_22","unstructured":"Baffa, M.F.O., Bachmann, L., Pereira, T.M., and Felipe, J.C. (2021, January 7\u20139). Hyperspectral Signal Analysis for Thyroid Neoplasm Typification on Infrared Spectrum. Proceedings of the IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), Piscataway, NJ, USA."},{"key":"ref_23","first-page":"537","article-title":"Detecting aggressive papillary thyroid carcinoma using hyperspectral imaging and radiomic features","volume":"12033","author":"Leitch","year":"2022","journal-title":"Proc. SPIE"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Braga, M.S., Gomes, O.F., Jaimes, R.F.V.V., Braga, E.R., Borysow, W., and Salcedo, W.J. (2019, January 26\u201330). Multispectral colorimetric portable system for detecting metal ions in liquid media. Proceedings of the 2019 4th International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT), S\u00e3o Paulo, Brazil.","DOI":"10.1109\/INSCIT.2019.8868861"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lopin, P., Nawsang, P., Laywisadkul, S., and Lopin, K.V. (2025). Evaluation of low-cost multi-spectral sensors for measuring chlorophyll levels across diverse leaf types. Sensors, 25.","DOI":"10.20944\/preprints202503.1183.v1"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.35713\/aic.v4.i1.1","article-title":"Artificial intelligence in the diagnosis of thyroid cancer: Recent advances and future directions","volume":"4","author":"Nagendra","year":"2023","journal-title":"Artif. Intell. Cancer"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Habchi, Y., Himeur, Y., Kheddar, H., Boukabou, A., Atalla, S., Chouchane, A., Ouamane, A., and Mansoor, W. (2023). AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions. Systems, 11.","DOI":"10.3390\/systems11100519"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104668","DOI":"10.1016\/j.jvcir.2025.104668","article-title":"Machine learning and transformers for thyroid carcinoma diagnosis","volume":"115","author":"Habchi","year":"2026","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108100","DOI":"10.1016\/j.cmpb.2024.108100","article-title":"Deep neural networks can differentiate thyroid pathologies on infrared hyperspectral images","volume":"247","author":"Baffa","year":"2024","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1177\/00037028241232440","article-title":"Near-Infrared Spectroscopy for Distinguishing Malignancy in Thyroid Nodules","volume":"78","author":"Zufry","year":"2024","journal-title":"Appl. Spectrosc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tholkappian, R., Sinha, S., Chinni, B., Rao, N., and Dogra, V.S. (2023, January 5\u20136). Computer-aided tissue characterization for detection of thyroid cancer using multi-wavelength photoacoustic imaging. Proceedings of the 2nd International Conference on Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal Processing (PCEMS), Nagpur, India.","DOI":"10.1109\/PCEMS58491.2023.10136084"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1097\/PAI.0000000000001095","article-title":"Reinventing Nuclear Histo-score Utilizing Inherent Morphologic Cutoffs: Blue-brown Color H-score (BBC-HS)","volume":"31","author":"Price","year":"2023","journal-title":"Appl. Immunohistochem. Mol. Morphol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dur\u00e1n-D\u00edaz, I., Sarmiento, A., Fond\u00f3n, I., Bodineau, C., Tom\u00e9, M., and Dur\u00e1n, R.V. (2024). A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining. Entropy, 26.","DOI":"10.3390\/e26020165"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e201700075","DOI":"10.1002\/jbio.201700075","article-title":"Dual light-emitting diode-based multichannel microscopy for whole-slide multiplane, multispectral and phase imaging","volume":"11","author":"Liao","year":"2018","journal-title":"J. Biophotonics"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"La Salvia, M., Torti, E., Lago, G., Leon, R., Gandolfi, R., Silveri, G., Rossella, M., Danese, G., Lago, P., and Leporati, F. (2023). Hyperspectral imaging acquisition set-up for medical applications. Hyperspectral Imaging and Applications II, SPIE.","DOI":"10.1117\/12.2647570"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rosa-Olmeda, G., Villa, M., Hiller-Vallina, S., Chavarrias, M., Pescador, F., and Gargini, R. (2024). A Microscope Setup and Methodology for Capturing Hyperspectral and RGB Histopathological Imaging Databases. Sensors, 24.","DOI":"10.3390\/s24175654"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"135210","DOI":"10.1016\/j.snb.2023.135210","article-title":"A centrifugal microfluidic system for automated detection of multiple heavy metal ions by aptamer-based colorimetric assay","volume":"403","author":"Zhou","year":"2024","journal-title":"Sens. Actuators B Chem."},{"key":"ref_38","unstructured":"ams OSRAM (2023). AS7343 14-Channel Multi-Spectral Sensor; Datasheet DS001046, ver. 6-00, ams OSRAM."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Braga, M.S., Jaimes, R.F.V.V., Borysow, W., Gomes, O.F., and Salcedo, W.J. (2017). Portable multispectral colorimeter for metallic ion detection and classification. Sensors, 17.","DOI":"10.20944\/preprints201706.0058.v1"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hariharan, J., Ampatzidis, Y., and Abdulridha, J. (2020, January 6\u20138). The Basis for Development of a Foundational Biomarker Reflectance Signature Database System for Plant Cell Identification, Disease Detection, and Classification Purposes. Proceedings of the 10th IEEE Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC47524.2020.9031170"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1177\/00037028211066327","article-title":"Statistical Considerations and Tools to Improve Histopathologic Protocols with Spectroscopic Imaging","volume":"76","author":"Mittal","year":"2022","journal-title":"Appl. Spectrosc."},{"key":"ref_42","first-page":"1282703","article-title":"Registration of hyperspectral images and mass spectrometry data for the correlation of tissue optical spectra and molecular profiles","volume":"12827","author":"Fei","year":"2024","journal-title":"Proc. SPIE"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1007\/978-3-031-46933-6_11","article-title":"Tumor Tissue Classification in Hyperspectral Histopathology Images Through Individual and Ensemble of Machine Learning Algorithms","volume":"Volume 96","year":"2024","journal-title":"XLVI Mexican Conference on Biomedical Engineering"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Durkee, M.S., Ai, J., Casella, G., Cao, T., Chang, A., Halper-Stromberg, A., Jabri, B., Clark, M.R., and Giger, M.L. (2024). Pseudo-spectral angle mapping for automated pixel-level analysis of highly multiplexed tissue image data. bioRxiv.","DOI":"10.1101\/2024.01.09.574920"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2526","DOI":"10.7150\/ijbs.83068","article-title":"Comprehensive Histopathology Imaging in Pancreatic Biopsies: High Definition Infrared Imaging with Machine Learning Approach","volume":"19","author":"Liberda","year":"2023","journal-title":"Int. J. Biol. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5880","DOI":"10.1039\/D0AN02155E","article-title":"Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets","volume":"146","author":"Tang","year":"2021","journal-title":"Analyst"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Fanjul-V\u00e9lez, F., Pamp\u00edn-Su\u00e1rez, S., and Arce-Diego, J.L. (2020). Application of Classification Algorithms to Diffuse Reflectance Spectroscopy Measurements for Ex Vivo Characterization of Biological Tissues. Entropy, 22.","DOI":"10.1117\/12.2508954"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"052912","DOI":"10.1117\/1.JBO.26.5.052912","article-title":"Machine learning to extract physiological parameters from multispectral diffuse reflectance spectroscopy","volume":"26","author":"Nguyen","year":"2021","journal-title":"J. Biomed. Opt."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e202200140","DOI":"10.1002\/jbio.202200140","article-title":"Distinguishing tumor from healthy tissue in human liver ex vivo using machine learning and multivariate analysis of diffuse reflectance spectra","volume":"15","author":"Reistad","year":"2022","journal-title":"J. Biophotonics"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"116955","DOI":"10.1016\/j.trac.2023.116955","article-title":"Optical spectroscopy and chemometrics in intraoperative tumor margin assessment","volume":"160","author":"Boichenko","year":"2023","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.trac.2014.02.016","article-title":"Current trends in machine-learning methods applied to spectroscopic cancer diagnosis","volume":"59","author":"Sattlecker","year":"2014","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"106003","DOI":"10.1117\/1.JBO.27.10.106003","article-title":"Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging","volume":"27","author":"Witteveen","year":"2022","journal-title":"J. Biomed. Opt."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3791214","DOI":"10.1155\/2016\/3791214","article-title":"A Support Vector Machine Classification of Thyroid Bioptic Specimens Using MALDI-MSI Data","volume":"2016","author":"Galli","year":"2016","journal-title":"Adv. Bioinform."},{"key":"ref_54","doi-asserted-by":"crossref","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":"Anal. Bioanal. Chem."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e202200227","DOI":"10.1002\/jbio.202200227","article-title":"Diseased thyroid tissue classification in OCT images using deep learning: Towards surgical decision support","volume":"16","author":"Tampu","year":"2023","journal-title":"J. Biophotonics"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Xanthopoulos, P., Pardalos, P.M., and Trafalis, T.B. (2013). Support Vector Machines in Robust Data Mining, Springer.","DOI":"10.1007\/978-1-4419-9878-1"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5915","DOI":"10.1039\/D0AN01328E","article-title":"A three-dimensional discriminant analysis approach for hyperspectral images","volume":"145","author":"Morais","year":"2020","journal-title":"Analyst"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Binaghi, E., Gallo, I., Boschetti, M., and Brivio, P.A. (2004). A neural adaptive model for hyperspectral data classification under minimal training conditions. Proceedings of the Image and Signal Processing for Remote Sensing X, SPIE.","DOI":"10.1117\/12.567950"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"104180","DOI":"10.1016\/j.infrared.2022.104180","article-title":"Heterogeneity classification based on hyperspectral transmission imaging and multivariate data analysis","volume":"123","author":"Li","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/978-3-031-29970-4_2","article-title":"Deep Recurrent Neural Network Performing Spectral Recurrence on Hyperspectral Images for Brain Tissue Classification","volume":"Volume 13879","author":"Villa","year":"2023","journal-title":"Design and Architecture for Signal and Image Processing (DASIP 2023)"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Hasbi, N.H., Bade, A., Chee, F.P., and Rumaling, M.I. (2022). Pattern Recognition for Human Diseases Classification in Spectral Analysis. Computation, 10.","DOI":"10.3390\/computation10060096"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Denk\u00e7eken, T., Canpolat, M., Ba\u015fsorgun, C.\u0130., and Baykara, M. (2014, January 16\u201317). Application of principal component analysis and linear discriminant analysis on elastic light single-scattering spectroscopic data for classification of prostate tissue: A preliminary study. Proceedings of the 2014 18th National Biomedical Engineering Meeting (BIYOMUT), Istanbul, Turkey.","DOI":"10.1109\/BIYOMUT.2014.7026364"},{"key":"ref_63","first-page":"933","article-title":"Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data","volume":"Volume 5517","author":"Strickert","year":"2009","journal-title":"Proceedings of the Bio-Inspired Systems: Computational and Ambient Intelligence (IWANN 2009)"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1366\/0003702991945920","article-title":"LDA-Guided Search Engine for the Nonsubjective Analysis of Infrared Microscopic Maps","volume":"53","author":"Mansfield","year":"1999","journal-title":"Appl. Spectrosc."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5935","DOI":"10.1039\/C8AN01303A","article-title":"Optimizing decision tree structures for spectral histopathology (SHP)","volume":"143","author":"Mu","year":"2018","journal-title":"Analyst"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Septiana, L., Suzuki, H., Ishikawa, M., Obi, T., Kobayashi, N., Ohyama, N., Wihardjo, E., and Andiani, D. (2019, January 23\u201327). Classification of Elastic and Collagen Fibers in H&E Stained Hyperspectral Images. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856371"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1089\/cmb.2007.0057","article-title":"Identifying variables responsible for clustering in discriminant analysis of data from infrared microspectroscopy of a biological sample","volume":"14","author":"Martin","year":"2007","journal-title":"J. Comput. Biol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1016\/j.trac.2009.07.007","article-title":"Review of the most common pre-processing techniques for near-infrared spectra","volume":"28","author":"Rinnan","year":"2009","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Trendafilov, N.T., and Gallo, M. (2021). Linear discriminant analysis (LDA). Multivariate Data Analysis on Matrix Manifolds, Springer.","DOI":"10.1007\/978-3-030-76974-1"},{"key":"ref_70","unstructured":"Cevikalp, H. (2006, January 16\u201319). Theoretical Analysis of Linear Discriminant Analysis Criteria. Proceedings of the 2006 IEEE 14th Signal Processing and Communications Applications, Antalya, Turkey."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Sifaou, H., Kammoun, A., and Alouini, M.-S. (2018, January 25\u201328). Improved LDA Classifier Based on Spiked Models. Proceedings of the 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Kalamata, Greece.","DOI":"10.1109\/SPAWC.2018.8446025"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"105136","DOI":"10.1016\/j.chemolab.2024.105136","article-title":"A new sub-class linear discriminant for miniature spectrometer based food analysis","volume":"250","author":"Nibouche","year":"2024","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"F\u00e9ret, J.-B., Asner, G.P., and Jacquemoud, S. (2011, January 6\u20139). Regularization of discriminant analysis for the study of biodiversity in humid tropical forests. Proceedings of the 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon, Portugal.","DOI":"10.1109\/WHISPERS.2011.6080945"},{"key":"ref_74","first-page":"463","article-title":"Evidence Accumulation to Identify Discriminatory Signatures in Biomedical Spectra","volume":"Volume 3581","author":"Bamgbade","year":"2005","journal-title":"Artificial Intelligence in Medicine (AIME 2005)"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"105163","DOI":"10.1016\/j.biosystems.2024.105163","article-title":"An efficient approach for identifying important biomarkers for biomedical diagnosis","volume":"237","author":"Huang","year":"2024","journal-title":"Biosystems"},{"key":"ref_76","first-page":"1","article-title":"Bayesian subset-based gene selection for biomarker discovery in high-dimensional data","volume":"2","author":"Paplomatas","year":"2025","journal-title":"Acad. Mol. Biol. Genom."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Pranckeviciene, E., Baumgartner, R., and Somorjai, R. (2004, January 26). Consensus-based identification of spectral signatures for classification of high-dimensional biomedical spectra. Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK.","DOI":"10.1109\/ICPR.2004.1334189"},{"key":"ref_78","first-page":"829","article-title":"SVM Optimization for Hyperspectral Colon Tissue Cell Classification","volume":"Volume 3217","author":"Rajpoot","year":"2004","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2004"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Prigent, S., Descombes, X., Zugaj, D., and Zerubia, J. (2010, January 14\u201316). Spectral analysis and unsupervised SVM classification for skin hyper-pigmentation classification. Proceedings of the 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Reykjavik, Iceland.","DOI":"10.1109\/WHISPERS.2010.5594917"},{"key":"ref_80","first-page":"22","article-title":"Organ Origin Identification Based on Fine Feature Analysis Using Support Vector Machines","volume":"12","author":"Tang","year":"2004","journal-title":"Int. J. Comput. Internet Manag."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1366\/000370210790572124","article-title":"Improving the Discriminatory Power of a Near-Infrared Microscopy Spectral Library with a Support Vector Machine Classifier","volume":"64","author":"Fearn","year":"2010","journal-title":"Appl. Spectrosc."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"69661V","DOI":"10.1117\/12.770306","article-title":"Support Vector Machines in hyperspectral imaging spectroscopy with application to material identification","volume":"6966","author":"Anabitarte","year":"2008","journal-title":"Proc. SPIE"},{"key":"ref_83","unstructured":"Wu, D., and Ma, C. (September, January 30). The Support Vector Machine (SVM) Based Near-Infrared Spectrum Recognition of Leaves Infected by the Leafminers. Proceedings of the First International Conference on Innovative Computing, Information and Control\u2014Volume I (ICICIC\u201906), Beijing, China."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"109126","DOI":"10.1016\/j.ress.2023.109126","article-title":"Support vector machine in structural reliability analysis: A review","volume":"233","author":"Roy","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Guido, R. (2024). An Overview on the Advancements of Support Vector Machine in the Field of Medicine. Information, 15.","DOI":"10.3390\/info15040235"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1007\/978-3-540-73044-6_112","article-title":"Detection of the cancerous tissue sections in the breast optical biopsy dataflow using neural networks","volume":"Volume 16","author":"Nuzhny","year":"2007","journal-title":"Proceedings of the 11th Mediterranean Conference on Medical and Biomedical Engineering and Computing 2007"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/978-3-031-25599-1_6","article-title":"A Kernel-Based Multilayer Perceptron Framework to Identify Pathways Related to Cancer Stages","volume":"Volume 13810","author":"Soleimanpoor","year":"2023","journal-title":"Machine Learning, Optimization, and Data Science"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"116804","DOI":"10.1016\/j.trac.2022.116804","article-title":"Deep learning for near-infrared spectral data modelling: Hypes and benefits","volume":"157","author":"Mishra","year":"2022","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s44258-024-00043-1","article-title":"Artificial intelligence on biomedical signals: Technologies, applications, and future directions","volume":"2","author":"Lee","year":"2024","journal-title":"Med-X"},{"key":"ref_90","unstructured":"Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A. (2021). Revisiting Deep Learning Models for Tabular Data. Proceedings of the NIPS\u201921: 35th International Conference on Neural Information Processing Systems (NeurIPS 2021), Curran Associates Inc."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Cui, R., Yu, H., Xu, T., Xing, X., Cao, X., Yan, K., and Chen, J. (2022). Deep Learning in Medical Hyperspectral Images: A Review. Sensors, 22.","DOI":"10.3390\/s22249790"},{"key":"ref_92","unstructured":"Tampu, I.E., Eklund, A., Johansson, K., Gimm, O., and Haj-Hosseini, N. (February, January 22). Classification of thyroid diseases in OCT images using convolutional neural networks. Proceedings of the Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XX, San Francisco, CA, USA."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"180409","DOI":"10.1109\/ACCESS.2019.2959036","article-title":"A Dynamic Rectified Linear Activation Units","volume":"7","author":"Hu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Banerjee, C., Mukherjee, T., and Pasiliao, E.L. (2019, January 18\u201320). An Empirical Study on Generalizations of the ReLU Activation Function. Proceedings of the 2019 ACM Southeast Conference, Kennesaw, GA, USA.","DOI":"10.1145\/3299815.3314450"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Kulathunga, N., Ranasinghe, N.R., Vrinceanu, D., Kinsman, Z., Huang, L., and Wang, Y. (2021). Effects of Nonlinearity and Network Architecture on the Performance of Supervised Neural Networks. Algorithms, 14.","DOI":"10.3390\/a14020051"},{"key":"ref_96","first-page":"33","article-title":"ReLU-Based Activations: Analysis and Experimental Study for Deep Learning","volume":"Volume 12882","year":"2021","journal-title":"Advances in Artificial Intelligence"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Banerjee, C., Mukherjee, T., and Pasiliao, E.L. (2020, January 2\u20134). The Multi-phase ReLU Activation Function. Proceedings of the 2020 ACM Southeast Conference, Tampa, FL, USA.","DOI":"10.1145\/3374135.3385313"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1177\/09603360221142821","article-title":"Perspectives on deep learning for near-infrared spectral data modelling","volume":"33","author":"Passos","year":"2022","journal-title":"NIR News"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1002\/mrm.29561","article-title":"Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias","volume":"89","author":"Rizzo","year":"2023","journal-title":"Magn. Reson. Med."},{"key":"ref_100","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_101","unstructured":"Zhang, Y., and Shen, J. (2024, January 4\u20139). Fingerprint Representation of Metabolite Magnetic Resonance Spectroscopy with Deep Learning. Proceedings of the 2024 ISMRM & ISMRT Annual Meeting & Exhibition (ISMRM), Singapore. Abstract."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1021\/acs.jcim.2c01535","article-title":"Intelligent Discrete Deep Learning Based Classification Methodology in Chemometrics","volume":"63","author":"Khashei","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"ref_103","unstructured":"Ennett, C.M., Frize, M., and Scales, N.B. (2003, January 24\u201326). Evaluation of the logarithmic-sensitivity index as a neural network stopping criterion for rare outcomes. Proceedings of the 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, Birmingham, UK."},{"key":"ref_104","first-page":"101","article-title":"Thyroid carcinoma detection on whole histologic slides using hyperspectral imaging and deep learning","volume":"12039","author":"Tran","year":"2022","journal-title":"Proc. SPIE"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"16590","DOI":"10.1038\/s41598-023-43856-7","article-title":"An eXplainable Artificial Intelligence analysis of Raman spectra for thyroid cancer diagnosis","volume":"13","author":"Bellantuono","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Gao, L., Wu, S., Wongwasuratthakul, P., Chen, Z., Cai, W., Li, Q., and Lin, L.L. (2024). Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples. Biosensors, 14.","DOI":"10.3390\/bios14080372"},{"key":"ref_107","first-page":"093505","article-title":"Detection and Margin Assessment of Thyroid Carcinoma with Microscopic Hyperspectral Imaging","volume":"9","author":"Tran","year":"2024","journal-title":"J. Biomed. Opt."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Edwards, K.T., Halicek, M., Little, J.V., Chen, A.Y., and Fei, B. (2021, January 14\u201317). Multiparametric Radiomics for Predicting the Aggressiveness of Papillary Thyroid Carcinoma Using Hyperspectral Images. Proceedings of the SPIE Medical Imaging, San Diego, CA, USA.","DOI":"10.1117\/12.2582147"},{"key":"ref_109","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_110","doi-asserted-by":"crossref","unstructured":"Mittal, P., Condina, M.R., Klingler-Hoffmann, M., Kaur, G., Oehler, M.K., Sieber, O.M., Palmieri, M., Kommoss, S., Brucker, S., and McDonnell, M.D. (2021). Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging. Cancers, 13.","DOI":"10.3390\/cancers13215388"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1038\/s41698-023-00399-4","article-title":"Deep learning generates synthetic cancer histology for explainability and education","volume":"7","author":"Dolezal","year":"2023","journal-title":"npj Precis. Oncol."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Wemmert, C., Kr\u00fcger, J.M., Forestier, G., Sternberger, L., Feuerhake, F., and Gan\u00e7arski, P. (2013, January 15\u201318). Stain unmixing in brightfield multiplexed immunohistochemistry. Proceedings of the 2013 IEEE International Conference on Image Processing (ICIP), Melbourne, Australia.","DOI":"10.1109\/ICIP.2013.6738232"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/s41377-022-00743-6","article-title":"Spectral imaging with deep learning","volume":"11","author":"Huang","year":"2022","journal-title":"Light Sci. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/14\/4448\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T03:36:12Z","timestamp":1784172972000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/14\/4448"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,13]]},"references-count":113,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["s26144448"],"URL":"https:\/\/doi.org\/10.3390\/s26144448","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,13]]}}}