Oral

Authors: Mrs. G. SANGEETHA LAKSHMI., Mrs. S. Hemalatha

Abstract: – Early and precise detection of oral cancer is critical for improving patient outcomes, yet conventional diagnostic methods often involve manual analysis, which can be slow and susceptible to human error. To overcome these limitations, this research introduces an automated detection system that combines deep learning for feature extraction with the Random Forest algorithm for classification. By analyzing medical images, the deep learning component identifies essential features such as texture, color inconsistencies, and irregular tissue structures. These features are then processed by the Random Forest classifier, which utilizes an ensemble of decision trees to enhance classification accuracy and minimize errors. Trained on a dedicated dataset of oral cancer images, the model effectively differentiates between malignant and benign tissues. Experimental findings reveal that this hybrid approach outperforms standard machine learning techniques, offering a faster and more dependable diagnostic tool to aid clinicians in early oral cancer detection and improve patient survival rates.

DOI: http://doi.org/