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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.08114 |
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| _version_ | 1866917719287267328 |
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| author | Rezaie, Shahriar Saberitabar, Neda Salehi, Elnaz |
| author_facet | Rezaie, Shahriar Saberitabar, Neda Salehi, Elnaz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08114 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Dental Diagnostics: Enhanced Convolution with Spatial Attention Mechanism Rezaie, Shahriar Saberitabar, Neda Salehi, Elnaz Computer Vision and Pattern Recognition 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. |
| title | Improving Dental Diagnostics: Enhanced Convolution with Spatial Attention Mechanism |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.08114 |