Oral Cancer Detection with Customized Deep Neural Network Based Transfer Learning Technique: A Comprehensive 2-D Image Analysis

Global health is often hampered by oral cancer because of its high prevalence and associated mortality rates, often exacerbated by delayed detection. This study proposes a novel deep structured learning based approach for oral cancer detection, integrating diversified image analysis of both microscopic and macroscopic images. Leveraging the ResNet architecture for transfer learning, the model is fine-tuned to handle the complexities of varied image modalities, ensuring robust feature extraction and accurate classification. Comprehensive pre-processing techniques address variability in image quality, while hyper-parameter optimization enhances performance. Experimental results demonstrate superior accuracy, precision, and recall across multiple datasets, showcasing our model's capability to generalize effectively. In this research we highlight the possibility of deep learning in closing the gap in diagnostics, offering a scalable, automated, and an constructive approach for the early recognition of oral cancer in diverse clinical settings.