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Novel Architecture of RPA In Oral Cancer Lesion Detection

Revana Magdy, Joy Naoum, Ali Hamdi

TL;DR

This study evaluates two RPA implementations, OC-RPAv1 and OC-RPAv2, using a test set of 31 images, showing that design patterns and batch processing can enhance scalability and reduce costs in oral cancer detection.

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

Novel Architecture of RPA In Oral Cancer Lesion Detection

TL;DR

This study evaluates two RPA implementations, OC-RPAv1 and OC-RPAv2, using a test set of 31 images, showing that design patterns and batch processing can enhance scalability and reduce costs in oral cancer detection.

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
Paper Structure (15 sections, 3 figures, 2 tables)

This paper contains 15 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. RPA Workflow
  3. Related Work
  4. Relationship Between RPA and AI in the Proposed Workflow
  5. Methodology
  6. Dataset Description
  7. Dataset PreProcessing
  8. Augmentation Flow Architecture
  9. Model Architecture
  10. Training Strategy
  11. Workflow Synchronization, Error Handling, and Data Security
  12. Experiments and Results
  13. Discussion
  14. Conclusion
  15. Future work

Figures (3)

  • Figure 1: The OC RPA workflow illustrates two parallel pipelines that converge at the same CNN model. The first pipeline processes a single image through the CNN model, while the second applies design patterns—specifically, the Singleton and Batch Strategy patterns—to process a batch of images.
  • Figure 2: Sample images from the dataset, showing different oral cancer lesion types (from , ref22 ).
  • Figure 3: Accessibility–Performance trade-off among UiPath, Automation Anywhere, OC-RPA v1, and OC-RPA v2. Dashed line denotes the Pareto frontier of optimal configurations.