Oral Cancer Detection

Deep learning approach for detecting oral cancer from smartphone images using YOLOv11 for object detection and EfficientNet for feature extraction and classification. The system was designed to provide early, accessible, and cost-effective cancer screening through mobile devices. Achieved high accuracy by applying advanced data augmentation techniques (rotation, brightness/contrast adjustments, and noise injection) and transfer learning with fine-tuning to handle limited medical datasets. The model was rigorously evaluated on real-world datasets, demonstrating strong performance in both precision and recall, and offering potential for deployment in telemedicine and low-resource healthcare environments.