Objectives: This study aimed to develop and validate a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Vision Transformers (ViT) to automatically classify maxillary sinus membrane morphologies on Cone-Beam Computed Tomography (CBCT) images, distinguishing between Normal, Flat, Polypoid, and Obstruction types. Methods: A dataset of 959 CBCT images was collected and categorized into four morphological classes: Normal, Flat, Polypoid and Obstruction. A custom hybrid model was developed, integrating a lightweight residual CNN for local feature extraction, learnable weighted feature fusion with a bidirectional feature pyramid network and a Transformer encoder for global context modeling. The performance of proposed model was compared against six different architectures, including ResNet50, MobileNetV3L and standard ViT models, using accuracy, precision, recall and F1-score metrics. Results: The proposed hybrid model achieved the highest overall accuracy of 98.44%, outperforming six strong CNN and ViT models including ResNet50 (97.92%) and ViT-B16 (86.46%) models. In class-wise analysis, the model demonstrated superior diagnostic capability, particularly for the “Obstruction” class, achieving 100% accuracy. High discrimination was also observed for “Flat” (98.21%) and “Polypoid” (98.04%) morphologies, confirming the model’s sensitivity to shape-based features. Conclusions: The proposed hybrid CNN-ViT model successfully classifies maxillary sinus membrane morphologies with high accuracy, effectively overcoming the limitations of standard ViT models on limited datasets. Detection of membrane morphology is vital for predicting surgical risks like membrane perforation and post-operative sinusitis. This model serves as a reliable clinical decision support tool, enabling clinicians to objectively assess specific risk factors before implant surgery and sinus floor elevation.

Background/Objectives: The aim of this study is to test different convolutional neural network (CNN) and Transformer-based models to detect and classify vertical misfit at the abutment-prosthesis interface on panoramic radiographs, and to develop a hybrid deep learning model enhanced with attention mechanisms. Methods: A dataset consisting of a total of 566 images, manually classified as 249 ‘fit’ and 317 ‘misfit’ cases by two experts, was created. Images were resized to 224 × 224 and divided into training, validation, and test groups. The deep learning model yielding the most successful results was determined as the backbone; a hybrid model was developed by integrating three different attention modules (SE, CBAM, and ECA) into this structure. Model performance was evaluated using accuracy, precision, sensitivity, and F1 score metrics. Results: CNN-based models (RegNetY-800MF, ConvNeXt-Tiny, EfficientNetV2-S, ResNet50) performed better than Transformer-based models (DeiT, Swin-Tiny) in all metrics. The proposed hybrid model exhibited the highest success among all tested models with a 99.12% accuracy rate. This model reached a 100% precision value in the misfit group and yielded no false positive results. The F1 scores of the hybrid model were recorded as 99.01% for the fit group and 99.21% for the misfit group. Conclusions: The findings of this study demonstrate that attention-enhancing deep learning frameworks have the potential to significantly improve the diagnostic utility of routine panoramic radiographs. It shows that panoramic imaging, when supported by advanced artificial intelligence, can provide valuable diagnostic support in detecting vertical misfit. The developed model has the potential to become a reliable clinical decision support system.

Background/Objectives: Early diagnosis of eye diseases is critically important for enhancing individuals’ quality of life and reducing the risk of vision loss. In this study, a deep learning-based hybrid model called MidFusionEfficientV2 has been proposed to classify eye diseases, including uveitis, conjunctivitis, cataract, eyelid drooping, and normal conditions. Methods: The model presents a dual-branch architecture that combines an RGB image branch with an EfficientNetV2-S architecture and a specialized texture branch based on Local Binary Pattern (LBP) transformation at an intermediate level. Thanks to the Squeeze-and-Excitation (SE) blocks integrated into the LBP branch, channel-based attention mechanisms have been activated, enhancing the prominence of textural features. The features obtained from the RGB and LBP branches were combined at an intermediate level and transferred to the classification stage. Results: Experimental studies on the five-class eye disease dataset from the Mendeley Data platform have shown that the proposed model outperformed six strong models (ResNetV2, ConvNeXt, DenseNet-121, EfficientNet-B1, MobileNetV3 Large, and EfficientNetV2-S) with an accuracy of 98%. Especially in the difficult-to-diagnose uveitis class, recall and F1 scores of 97% and 94%, respectively, were achieved. Conclusions: The results show that a moderate combination of color and texture features significantly improves classification performance, and that MidFusionEfficientV2 offers a reliable and effective solution for the automatic diagnosis of eye diseases.

Background/Objectives: Early diagnosis of periodontal disease is critical to prevent tissue destruction and tooth loss. This study evaluates six modern deep learning architectures for four-class radiographic periodontal bone loss grading on panoramic radiographs. Methods: This retrospective study analyzed 1053 images. Two periodontologists independently classified each image according to the most severely affected tooth into four bone loss categories: Healthy, Mild (50%). Six convolutional, transformer, and hybrid backbones (ConvNeXt-Tiny, MaxViT-Tiny, EfficientViT-B1, FocalNet-Tiny, CAFormer-S18, and DaViT-Tiny) were trained on a 70/10/20 stratified split using an NVIDIA Tesla T4 GPU, the AdamW optimizer, Cosine Annealing, and early stopping. Performance was assessed using standard classification metrics, Matthews Correlation Coefficient, Cohen’s and Quadratic Weighted Kappa, Expected Calibration Error, Area Under the Curve (AUC), McNemar’s test, and bootstrap 95% confidence intervals. Results: FocalNet-Tiny achieved the highest numerical scores among the tested models, reaching 83.41% accuracy, 84.70% balanced accuracy, 83.50% macro-F1, and 0.9610 OvR AUC. The p-values obtained from the comparisons of the models were all above 0.05, indicating that the classification performances of the evaluated models are quite similar. Bootstrap analysis confirmed stable prediction metrics for the top-scoring model. Conclusions: This study provides a controlled benchmark of modern deep learning backbones for four-class radiographic periodontal bone loss grading. FocalNet-Tiny achieved the highest numerical metrics and stable bootstrap estimates, yet performed comparably to other contemporary architectures such as ConvNeXt-Tiny. Direct radiographic classification shows promise for automated radiographic pre-screening; however, multi-center external validation and integration with clinical periodontal parameters are required before clinical implementation.

Background/Objectives: The patency and anatomical location of the maxillary sinus ostium are critical for preventing postoperative complications in dental implant planning and sinus lift surgeries in the posterior maxilla. Narrowing or obstruction of the ostium carries risks, including the development of acute/chronic sinusitis and bone graft failure after surgery. These risks must be carefully evaluated using preoperative radiographic images. It is time-consuming for physicians to manually perform this process, and details are overlooked due to a lack of clinical experience, which can increase surgical risks. Methods: This study aims to overcome these clinical challenges and improve the reliability of radiographic evaluation. In this study, a hybrid deep learning model is proposed for the automatic detection of the maxillary sinus ostium. The proposed model combines the local feature extraction power of CNN-based models with the global context modeling capabilities of transformer-based models, creating an effective model. Additionally, the gated fusion technique efficiently combines features from various designs, significantly enhancing classification performance. Results: The proposed model was compared with six different ViT and CNN architectures established in the literature. While the highest test accuracy among pre-trained models was 89.36%, the proposed hybrid model achieved 95.03%, demonstrating strong clinical diagnostic performance. Conclusions: Based on the performance metrics obtained, we believe the proposed model can be used to determine the patency of the maxillary sinus ostium. This will lighten the workload for specialists and minimize traditional errors.

Background/Objectives : Oral squamous cell carcinoma (OSCC) has high mortality rates and leads to serious health problems when diagnosed late. This situation is considered a public health problem. Histopathological examination, which is an important point in the diagnosis of the disease, is a time-consuming and manual process that requires expertise. Methods: This study presents a deep learning-based approach for the automated classification of normal oral epithelium and oral squamous cell carcinoma (OSCC) from histopathological images. The performance of state-of-the-art architectures such as Vision Transformer (ViT), CLIP, ConvNextV2, Deit, and Dinov2 was comparatively analyzed. Based on the results, a hybrid architecture combining the strengths of the models is proposed. Results: In experiments conducted on a dataset of 696 histopathological images, the ConvNextV2 and ViTL16 architectures stood out among the basic models with accuracy rates around 84%. However, the most significant contribution of this study is the proposed method, which combines the global context capability of Transformer-based models with the local feature extraction power of CNN-based models using the Efficient Channel Attention (ECA)—Gated Features model. This hybrid model, created by integrating the ViTL16, Dinov2, and ConvNextV2 architectures, achieved 89.95% accuracy, 89.82% F1 score, and 89.95% sensitivity with a KNN classifier, outperforming the baseline models in the literature. Conclusions: The results obtained demonstrate that the fusion of multiple architectures increases diagnostic reliability in medical image analysis and can assist pathologists as a decision support mechanism.