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Novel Architecture of RPA In Oral Cancer Lesion Detection

arXiv 2603.10928 ↗First posted 11 Mar 2026cs.CVcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 20 references that are in the map. First posted on arXiv 11 Mar 2026.

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References · 3

  1. 2020On the Evaluation of Intelligent Process Automationcited by 6
  2. 2024LMV-RPA: Large Model Voting-based Robotic Process Automationcited by 5
  3. 2025E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processingcited by 5

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