Development of Oral Cancer Detection Technique: A Comprehensive Approach Using CNNs and TensorFlow Lite

Oral Cancer has been a great threat to many, working as a silent predator, oral cancer in one of the leading contributors in overall cancer stats. Improving patient outcomes requires early diagnosis and screening, but access to prompt and efficient diagnostic techniques is still restricted, especially in environments with limited resources. The creation and incorporation of a convolutional neural network (CNN)-based model for oral cancer detection into a mobile application is shown in this research. This model, which was developed with TensorFlow and tuned for TensorFlow Lite deployment, makes oral cancer screening accessible, affordable, and real-time. The architecture of the model is specifically engineered to detect early indications of malignancy with exceptional precision, thereby substantially reducing the obstacles to efficient mouth cancer screening. This tool could significantly improve oral cancer outcomes worldwide by incorporating cutting-edge machine learning techniques into common technology, making it a vital step in the fight against this fatal illness.