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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.10928 |
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| _version_ | 1866917333164883968 |
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| author | Magdy, Revana Naoum, Joy Hamdi, Ali |
| author_facet | Magdy, Revana Naoum, Joy Hamdi, Ali |
| contents | Accurate and early detection of oral cancer lesions is crucial for effective diagnosis and treatment. This study evaluates two RPA implementations, OC-RPAv1 and OC-RPAv2, using a test set of 31 images. OC-RPAv1 processes one image per prediction in an average of 0.29 seconds, while OCRPAv2 employs a Singleton design pattern and batch processing, reducing prediction time to just 0.06 seconds per image. This represents a 60-100x efficiency improvement over standard RPA methods, showcasing that design patterns and batch processing can enhance scalability and reduce costs in oral cancer detection |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10928 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Novel Architecture of RPA In Oral Cancer Lesion Detection Magdy, Revana Naoum, Joy Hamdi, Ali Computer Vision and Pattern Recognition Accurate and early detection of oral cancer lesions is crucial for effective diagnosis and treatment. This study evaluates two RPA implementations, OC-RPAv1 and OC-RPAv2, using a test set of 31 images. OC-RPAv1 processes one image per prediction in an average of 0.29 seconds, while OCRPAv2 employs a Singleton design pattern and batch processing, reducing prediction time to just 0.06 seconds per image. This represents a 60-100x efficiency improvement over standard RPA methods, showcasing that design patterns and batch processing can enhance scalability and reduce costs in oral cancer detection |
| title | Novel Architecture of RPA In Oral Cancer Lesion Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.10928 |