Novel Architecture of RPA In Oral Cancer Lesion Detection
This work addresses the need for faster and more scalable oral cancer detection, which is crucial for early diagnosis and treatment, though it appears incremental as it builds on existing RPA methods with design patterns and batch processing.
The study tackled the problem of improving efficiency in oral cancer lesion detection by evaluating two RPA implementations, OC-RPAv1 and OC-RPAv2, with OC-RPAv2 achieving a prediction time of 0.06 seconds per image, representing a 60-100x efficiency improvement over standard methods.
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