Multi-Model Stacking Ensemble Approach for Improving Oral Cancer Diagnosis

The difficulty of early identification of oral cancer makes it a widespread health concern that often results in treatment delays and worse patient survival rates. Our work suggests a unique ensemble strategy for oral cancer identification as a solution to this urgent issue. Utilizing the strengths of three deep learning models—EfficientNetB0, EfficientNetB3, and InceptionV3—we increase the precision and dependability of diagnostics for oral cancer. To improve accuracy, we use stacked ensemble learning. To train a meta-learner, features are taken from a validation set and layered. The model performs better than individual models when tested on independent test sets as well as validation test sets. This study highlights the critical role that ensemble learning plays in enhancing the precision and dependability of oral cancer detection while also advancing machine learning approaches in the area of medical image analysis.