{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T19:24:41Z","timestamp":1789586681682,"version":"build-2803163510"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Information Application Support Software Engineering Technology Research Center of China","award":["2024RJGC-Y02"],"award-info":[{"award-number":["2024RJGC-Y02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The accurate detection of dental caries is often hindered by modality-specific imaging challenges, such as illumination artifacts in intra-oral photographs and low lesion contrast in radiographs. This study proposes a comprehensive framework comprising three key components: (1) HybridAugment+, an entropy-guided adaptive augmentation strategy that applies stronger transformations to low-information images; (2) DBAttNet, a dual-branch attention network featuring illumination\u2013reflection aware attention (IRAA) for photographs and contrast\u2013frequency-aware attention (CFA) for radiographs; and (3) a CAM-based explainability method, selected through a systematic evaluation of five advanced techniques. This study utilized two datasets derived from public sources, comprising 639 intra-oral photographs (481 caries, 158 healthy) and 456 radiographs (268 caries, 188 healthy). These were annotated by two dentists, with established inter-rater reliability (\u03ba = 0.82 for photographs, \u03ba = 0.79 for radiographs). The experimental results demonstrate that HybridAugment+ improved performance over conventional augmentation by up to 8.72% on photographs and 7.67% on radiographs. Furthermore, DBAttNet achieved F1-scores of 97.90% on photographs and 95.72% on radiographs, outperforming ResNet50, InceptionV3, MSDNet, DCANet, and ARM-Net. A comparative evaluation identified XGrad-CAM as the most suitable explainability method, with optimal visualization thresholds of 30% for photographs and 20% for radiographs. Generalization experiments on ophthalmology (APTOS 2019, Messidor-2) and chest radiography datasets (Kermany CXR, NIH ChestX-ray14) demonstrated consistent performance gains over domain-specific methods (DT-Net, ConvNeXt-Tiny). These results confirm that the core design principles effectively transfer to other modalities facing analogous imaging challenges.<\/jats:p>","DOI":"10.3390\/jimaging12050207","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T12:33:37Z","timestamp":1778589217000},"page":"207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Dual-Branch Deep Learning Framework with Explainability for Dental Caries Classification Using Intra-Oral Photographs and Radiographs"],"prefix":"10.3390","volume":"12","author":[{"given":"Lijuan","family":"Ren","sequence":"first","affiliation":[{"name":"School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"Sichuan Province Engineering Technology Research Center of Support Software of Informatization Application, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinjing","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6637196","DOI":"10.1155\/2021\/6637196","article-title":"Prevalence of Dental Caries and Its Associated Factors among Primary School Children in Ethiopia","volume":"2021","author":"Shitie","year":"2021","journal-title":"Int. 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