Optimizing Oral Cancer Detection: A Hybrid Feature Fusion using Local Binary Pattern and CNN

One of the most hazardous diseases is cancer, which poses a significant threat to human health due to its ability to manifest in various parts of the body as abnormal, proliferative cells. In the case of oral cancer, diverse behavioral patterns may be observed, making early detection and accurate prognosis crucial for effective and timely treatment. Multi-Layer Visual Feature Fusion (MLVFF) is a novel feature fusion technique that is explor to develop a system to detect and predict oral cancer at its earliest, most treatable stages. MLVFF combines Local Binary Patterns (LBP) characteristics with Convolutional Neural Network (CNN). More precisely, MLVFF may successfully improve the discriminating strength of features for Oral Cancer image recognition by combining the LBP and CNN versions, respectively. Test accuracy for the suggested model is 98.00%. In addition, we draw attention to emerging research questions in the field of cancer sickness, offering relevant data for further research that will improve cancer detection and treatments.