Novel Architecture of RPA In Oral Cancer Lesion Detection

• URL: http://arxiv.org/abs/2603.10928v1
• Date: Wed, 11 Mar 2026 16:12:27 GMT
• Title: Novel Architecture of RPA In Oral Cancer Lesion Detection
• Abstract summary: This study evaluates two RPA implementations, OC-RPAv1 and OC-RPAv2, using a test set of 31 images.<n>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.
• Score: 0.25489046505746704
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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

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