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Black classification. This study aimed to develop and evaluate CBMNet, a dual-attention enhanced ConvNeXt-Tiny model, for automated classification of G.V. Black Classes I-III using intraoral periapical radiographs. A total of 1103 anonymized periapical radiographs were retrospectively collected from the Sibar Institute of Dental Sciences, India, covering G.V. Black Class I (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009408), Class II (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009490), and Class III (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009205). To address class imbalance, minority classes were supplemented with high-fidelity synthetic images generated via StyleGAN2-ADA, validated using BRISQUE scores and blinded expert review. Images were pre-processed with CLAHE and median filtering, and CBMNet was implemented by integrating Convolutional Block Attention Module (CBAM) and Multi-Scale Attention Module (MSAM) into a ConvNeXt-Tiny backbone. Hyperparameters were optimized using Particle Swarm Optimization (PSO). Performance was evaluated through stratified 5-fold cross-validation, ablation studies, and a held-out real-image test set, with additional robustness testing via test-time augmentation(TTA). CBMNet achieved a mean validation accuracy of 93.26% ( 0.81)across folds and a final held-out test accuracy of 92% with TTA. Class-wise evaluation showed high precision (Class I:0.90, Class II:0.87, Class III:0.99), recall (Class I:0.94, Class II:0.90, Class III:0.91), and F1-scores (Class I:0.92, Class II:0.89, Class III:0.95). Ablation analysis confirmed the complementary contributions of CBAM, MSAM, and TTA. Compared with baseline models (ResNet50, EfficientnetB0, DenseNet121), CBMNet consistently outperformed in overall and class-specific metrics. The proposed CBMNet framework demonstrated robust diagnostic performance for automated classification of G.V. Black Classes I-III from periapical radiographs, with accuracy and class-wise metrics exceeding 90%. By integrating dual-attention mechanisms, GAN-based augmentation, and PSO-driven optimization, CBMNet provides a reliable, interpretable, and clinically relevant tool that may support early detection and standardized diagnosis of dental caries. Future studies with multi-centre datasets and prospective clinician comparisons are warranted to further validate clinical applicability.\n                  <\/jats:p>","DOI":"10.1038\/s41598-025-22975-3","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T15:00:59Z","timestamp":1762786859000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["CBMNet: a dual-attention enhanced ConvNeXt model for accurate G.V. Black type I\u2013III classification in intraoral periapical radiographs"],"prefix":"10.1038","volume":"15","author":[{"given":"Aneetta Joy","family":"Parathanath","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manimaran","family":"A","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"22975_CR1","doi-asserted-by":"publisher","first-page":"105105","DOI":"10.1016\/j.jdent.2024.105105","volume":"147","author":"V Szab\u00f3","year":"2024","unstructured":"Szab\u00f3, V. et al. Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs. J Dent. 147, 105105 (2024).","journal-title":"J Dent"},{"key":"22975_CR2","doi-asserted-by":"crossref","unstructured":"Sajad, M., Shafi, I. & Ahmad, J. Automatic lesion detection in periapical X-rays. 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