# Novel Architecture of RPA In Oral Cancer Lesion Detection

arXiv: https://arxiv.org/abs/2603.10928 · HTML: https://mlsanity.com/paper/2603.10928/

## 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.

## Builds on (most cited first, 3 of 3)

- [On the Evaluation of Intelligent Process Automation](https://mlsanity.com/paper/2001.02639/) (2020)
- [LMV-RPA: Large Model Voting-based Robotic Process Automation](https://mlsanity.com/paper/2412.17965/) (2024)
- [E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing](https://mlsanity.com/paper/2505.20733/) (2025)

## 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