Automatic Detection of Sinus Membrane Thickness Using a Hybrid Deep Learning Model Integrating Multiple Attention Mechanisms

Background/Objectives: In dental implant surgeries, automatic detection and classification of maxillary sinus membrane thickening from Cone-Beam Computed Tomography (CBCT) images used for diagnosis and treatment planning, using an artificial intelligence-based system, will both reduce specialists’ error rates and enable faster processing. Therefore, in this study, a unique model was developed to automatically detect maxillary sinus membrane thickening from CBCT images. Methods: The first step of the developed model is data preprocessing. Then, feature extraction is performed using the backbone Convolutional Neural Network (CNN). The obtained features are processed in parallel with Squeeze-and-Excitation (SE), Convolutional Block Attention Module (CBAM), and Efficient Channel Attention (ECA) attention mechanisms. In this stage, channel, spatial, and local relationships are better represented. The combined features are passed to a Multilayer Perceptron (MLP) with dropout to mitigate overlearning, and the final class estimation is performed by a softmax layer. Results: Thanks to the attention mechanisms employed in the developed hybrid model, both the model’s representational power and its classification performance have improved. The model developed to automatically detect and classify maxillary sinus membrane thickening from CBCT images achieved a test accuracy rate of 99.43%. Conclusions: The developed model was compared with pre-trained models accepted in the literature and demonstrated superior performance. The current results demonstrate that the proposed model exhibits high performance in classifying maxillary sinus membrane thickness in a single-center CBCT dataset. However, independent external validation and prospective clinical evaluation are necessary to determine its potential for clinical use.