{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:14:22Z","timestamp":1786115662135,"version":"build-2736575974"},"reference-count":29,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"KAU Endowment (WAQF), king Abdulaziz University"},{"id":[{"id":"https:\/\/ror.org\/02ma4wv74","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Dental caries is one of the most common oral diseases worldwide, and early diagnosis is essential for effective treatment. However, detecting carious lesions in panoramic radiographs is challenging because of low image contrast, anatomical complexity, and overlapping structures. This study aimed to develop an improved object detection framework for dental caries localization in panoramic radiographs. Methods: A modified YOLOv8 architecture was developed by integrating ConvNeXtV2 blocks into the Cross-Stage Partial (C2f) modules to enhance feature extraction and gradient propagation. To improve detection performance and reduce overfitting, key training hyperparameters were optimized using a genetic algorithm. The proposed model was evaluated on a dataset of 474 panoramic dental radiographs annotated by experts using bounding boxes. Results: The optimized model achieved a box precision of 78.4%, a recall of 53.6%, and a mean average precision at 50% intersection-over-union threshold (AP50) of 62.6%. Compared with the baseline YOLOv8 model, the proposed approach improved precision and AP50. Visual and quantitative analyses demonstrated that the ConvNeXtV2-enhanced architecture enabled more accurate localization of carious regions. Conclusions: The results indicate that combining ConvNeXtV2-based architectural enhancement with genetic algorithm-based hyperparameter optimization is an effective strategy for dental caries localization in panoramic radiographs. Although recall remains a limitation, the proposed framework shows potential as a computer-aided diagnostic tool for supporting clinical caries assessment.<\/jats:p>","DOI":"10.3390\/diagnostics16152454","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T15:51:23Z","timestamp":1785772283000},"page":"2454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Genetic Algorithm-Optimized ConvNeXtV2-YOLOv8 Framework for Dental Caries Detection in Panoramic Radiographs"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5788-5629","authenticated-orcid":false,"given":"Nebras","family":"Sobahi","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2835-506X","authenticated-orcid":false,"given":"Deniz Bora","family":"K\u00fc\u00e7\u00fck","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Computer and Information Sciences Faculty, Samsun University, 55000 Samsun, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaz\u0131m","family":"K\u0131l\u0131\u00e7","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligent Engineering, Computer and Information Sciences Faculty, Samsun University, 55000 Samsun, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3654-040X","authenticated-orcid":false,"given":"Anda\u00e7","family":"\u0130mak","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Faculty of Engineering, Munzur University, 62000 Tunceli, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adalet","family":"\u00c7elebi","sequence":"additional","affiliation":[{"name":"Oral and Maxillofacial Surgery Department, Faculty of Dentistry, Mersin University, 33000 Mersin, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yazyd","family":"Alghamedi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9561-0036","authenticated-orcid":false,"given":"Cafer","family":"Yazicioglu","sequence":"additional","affiliation":[{"name":"Department of Construction, Vocational School of Technical Sciences, Batman University, 72000 Batman, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2377-4979","authenticated-orcid":false,"given":"Muammer","family":"T\u00fcrko\u011flu","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Computer and Information Sciences Faculty, Samsun University, 55000 Samsun, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1614-2639","authenticated-orcid":false,"given":"Abdulkadir","family":"\u015eeng\u00fcr","sequence":"additional","affiliation":[{"name":"Department of Electrical-Electronics Engineering, Faculty of Technology, Firat University, 23100 Elazig, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Barani-Sve\u00e7la, M., and Buleshkaj, S. (2024). Etiopathogenesis of Dental Caries, IntechOpen.","DOI":"10.5772\/intechopen.114225"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Spatafora, G., Li, Y., He, X., Cowan, A., and Tanner, A.C.R. (2024). The Evolving Microbiome of Dental Caries. Microorganisms, 12.","DOI":"10.3390\/microorganisms12010121"},{"key":"ref_3","first-page":"56","article-title":"The global, regional, and national burden of oral disorders in 204 countries and territories, 1990\u20132021, and projections to 2050: A systematic analysis for the Global Burden of Disease Study 2021","volume":"1","author":"Wang","year":"2025","journal-title":"Regenesis Repair Rehabil."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1177\/0022034519871884","article-title":"Caries Detection with Near-Infrared Transillumination Using Deep Learning","volume":"98","author":"Casalegno","year":"2019","journal-title":"J. Dent. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lian, L., Zhu, T., Zhu, F., and Zhu, H. (2021). Deep Learning for Caries Detection and Classification. Diagnostics, 11.","DOI":"10.3390\/diagnostics11091672"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"790","DOI":"10.53730\/ijhs.v7nS1.14277","article-title":"Dental caries and non caries detection using deep learning","volume":"7","author":"Khan","year":"2023","journal-title":"Int. J. Health Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1111\/odi.13735","article-title":"Development and evaluation of deep learning for screening dental caries from oral photographs","volume":"28","author":"Zhang","year":"2022","journal-title":"Oral Dis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"16807","DOI":"10.1038\/s41598-021-96368-7","article-title":"Deep learning for early dental caries detection in bitewing radiographs","volume":"11","author":"Lee","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3991\/ijoe.v19i18.45133","article-title":"Automated Detection of Dental Caries from Oral Images using Deep Convolutional Neural Networks","volume":"19","author":"Lasri","year":"2023","journal-title":"Int. J. Online Biomed. Eng. (iJOE)"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"18320","DOI":"10.1109\/ACCESS.2022.3150358","article-title":"Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network","volume":"10","author":"Imak","year":"2022","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Oztekin, F., Katar, O., Sadak, F., Yildirim, M., Cakar, H., Aydogan, M., Ozpolat, Z., Yildirim, T.T., Yildirim, O., and Faust, O. (2023). An Explainable Deep Learning Model to Prediction Dental Caries Using Panoramic Radiograph Images. Diagnostics, 13.","DOI":"10.3390\/diagnostics13020226"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Thanh, M.T.G., Van Toan, N., Ngoc, V.T.N., Tra, N.T., Giap, C.N., and Nguyen, D.M. (2022). Deep Learning Application in Dental Caries Detection Using Intraoral Photos Taken by Smartphones. Appl. Sci., 12.","DOI":"10.3390\/app12115504"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"27659","DOI":"10.1007\/s11042-023-14435-9","article-title":"PaXNet: Tooth segmentation and dental caries detection in panoramic X-ray using ensemble transfer learning and capsule classifier","volume":"82","author":"Haghanifar","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s44196-026-01179-1","article-title":"Transformer-based Bilateral Encoder for Teeth Recognition and Bone Loss in Digital Radiographs","volume":"19","author":"Kotwal","year":"2026","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. arXiv.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. arXiv.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_18","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2026, May 19). Ultralytics YOLOv8. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_19","unstructured":"Guijin, H., Wang, R., Zhou, M., and Li, J. (2026, May 19). Enhancing Semantic and Spatial Information Yolov8. Available at SSRN 4797816. Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4797816."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5","DOI":"10.33640\/2405-609X.3339","article-title":"YOLOv8-CAB: Improved YOLOv8 for Real-time object detection","volume":"10","author":"Talib","year":"2024","journal-title":"Karbala Int. J. Mod. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, H., Wu, A., Jiang, Z., Liu, F., and Luo, M. (2024). Improving Object Detection in YOLOv8n with the C2f-f Module and Multi-Scale Fusion Reconstruction. 2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), IEEE.","DOI":"10.1109\/IMCEC59810.2024.10575292"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.3390\/vehicles6030065","article-title":"Comparative Analysis of YOLOv8 and YOLOv10 in Vehicle Detection: Performance Metrics and Model Efficacy","volume":"6","author":"Geetha","year":"2024","journal-title":"Vehicles"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, P., and Li, H. (2025). An Improved YOLOv5s-Based Algorithm for Unsafe Behavior Detection of Construction Workers in Construction Scenarios. Appl. Sci., 15.","DOI":"10.3390\/app15041853"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103692","DOI":"10.1016\/j.rineng.2024.103692","article-title":"An innovative deep learning framework for skin cancer detection employing ConvNeXtV2 and focal self-attention mechanisms","volume":"25","author":"Ozdemir","year":"2025","journal-title":"Results Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Niu, J., Liu, C., Zhang, L., and Liao, Y. (2019). Remaining Useful Life Prediction of Machining Tools by 1D-CNN LSTM Network. 2019 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE.","DOI":"10.1109\/SSCI44817.2019.9002993"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1109\/41.538609","article-title":"Genetic algorithms: Concepts and applications [in engineering design]","volume":"43","author":"Man","year":"1996","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Holland, J.H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press.","DOI":"10.7551\/mitpress\/1090.001.0001"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1007\/s12065-023-00822-6","article-title":"Genetic algorithms: Theory, genetic operators, solutions, and applications","volume":"17","author":"Alhijawi","year":"2024","journal-title":"Evol. Intell."},{"key":"ref_29","unstructured":"Goldberg, D.E. (2013). Genetic Algorithms, Pearson Education India."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/15\/2454\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T04:40:37Z","timestamp":1785991237000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/15\/2454"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,3]]},"references-count":29,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["diagnostics16152454"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16152454","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,3]]}}}