Novel Architecture of RPA In Oral Cancer Lesion Detection

2026-03-11

AI Summary

This study addresses the challenges of low efficiency and diagnostic delays in oral cancer lesion detection by proposing two novel RPA architectures, OC-RPAv1 and OC-RPAv2. These models innovatively integrate the singleton design pattern with batch image processing to optimize the prediction pipeline. The proposed approach significantly enhances system scalability while reducing computational overhead. Notably, OC-RPAv2 achieves a per-image prediction time of 0.06 seconds—60 to 100 times faster than conventional RPA methods—without compromising detection accuracy, thereby enabling highly efficient early diagnosis of oral cancer.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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

Problem

• oral cancer
• lesion detection
• early detection
• diagnosis
• RPA

Innovation

• RPA
• Singleton design pattern
• batch processing
• oral cancer detection
• efficiency improvement