Empirical Evaluation of AI Based Oral Cancer Identification Scheme Using Learning Assisted Medical Image Processing Scheme

Early detection is essential for enhancing patient outcomes, as oral cancer continues to be a substantial health challenge. This manuscript introduces the Optimized Neural Learning and Classification Principle (ONLCP), a novel AI-based oral cancer identification scheme that incorporates medical image processing techniques and deep learning. Our method utilizes smart to analyze and classify medical images, thereby enabling the early detection of oral cancer. The efficiency of the proposed scheme is assessed by cross-validating it with the Convolutional Neural Network (CNN). The model is refined by this AI-based logic, which adapts to a variety of image characteristics and improves detection precision, while the ONLCP is employed for feature extraction and initial classification of oral lesions. The experimental results indicate that the AI-based scheme ONLCP obtains a prediction accuracy of 97.86%, with substantial enhancements in sensitivity and specificity when contrasted with conventional methods. The model's continuous development and adaptive learning are enabled by the integration of learning-assisted techniques, resulting in more precise and dependable cancer identification. Our results emphasize the potential of integrating AI with advanced image processing techniques to resolve critical challenges in medical diagnostics. This research has the potential to improve patient care and treatment outcomes by contributing to the growth of more efficient and scalable techniques to early oral cancer identification.