Saved in:
Bibliographic Details
Main Authors: Magdy, Revana, Naoum, Joy, Hamdi, Ali
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2603.10928
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866917333164883968
author Magdy, Revana
Naoum, Joy
Hamdi, Ali
author_facet Magdy, Revana
Naoum, Joy
Hamdi, Ali
contents 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
format Preprint
id arxiv_https___arxiv_org_abs_2603_10928
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Novel Architecture of RPA In Oral Cancer Lesion Detection
Magdy, Revana
Naoum, Joy
Hamdi, Ali
Computer Vision and Pattern Recognition
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
title Novel Architecture of RPA In Oral Cancer Lesion Detection
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2603.10928