  Deep learning has emerged as a transformative tool in healthcare, offering
significant advancements in dental diagnostics by analyzing complex imaging
data. This paper presents an enhanced ResNet50 architecture, integrated with
the SimAM attention module, to address the challenge of limited contrast in
dental images and optimize deep learning performance while mitigating
computational demands. The SimAM module, incorporated after the second ResNet
block, refines feature extraction by capturing spatial dependencies and
enhancing significant features. Our model demonstrates superior performance
across various feature extraction techniques, achieving an F1 score of 0.676
and outperforming traditional architectures such as VGG, EfficientNet,
DenseNet, and AlexNet. This study highlights the effectiveness of our approach
in improving classification accuracy and robustness in dental image analysis,
underscoring the potential of deep learning to enhance diagnostic accuracy and
efficiency in dental care. The integration of advanced AI models like ours is
poised to revolutionize dental diagnostics, contributing to better patient
outcomes and the broader adoption of AI in dentistry.
