Novel Architecture of RPA In Oral Cancer Lesion Detection

TL;DR

This paper introduces two improved RPA-based methods for oral cancer lesion detection, significantly increasing processing speed and efficiency through design pattern and batch processing enhancements.

Contribution

The study presents OC-RPAv2, a novel RPA implementation that employs Singleton pattern and batch processing to drastically improve detection speed and scalability.

Findings

OC-RPAv1 processes images in 0.29 seconds each.

OC-RPAv2 reduces processing time to 0.06 seconds per image.

Efficiency improved by 60-100 times over standard RPA methods.

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