ORAL CANCER DETECTION

Abstract: Oral cancer is one of the most common and life-threatening cancers worldwide, particularly in developing regions where tobacco consumption, alcohol usage, and poor oral hygiene are prevalent. Early detection of oral cancer significantly improves survival rates; however, conventional diagnostic methods rely heavily on clinical examination and biopsy procedures, which are invasive, time-consuming, and dependent on expert availability. This project presents an intelligent Oral Cancer Detection System using machine learning and deep learning techniques to assist in early screening. The proposed system employs a Convolutional Neural Network (CNN) for analyzing oral cavity images to classify them as cancerous or non-cancerous. In addition, a machine learning-based clinical prediction model evaluates patient risk factors such as age, tobacco usage, alcohol consumption, and the presence of oral lesions. By integrating image-based analysis with clinical data evaluation, the system enhances diagnostic reliability and decision support. The developed models are deployed using a Streamlit-based web application that allows users to upload oral images and enter clinical details for real-time prediction. Experimental results demonstrate that the image-based model achieves high classification accuracy, while the clinical model effectively supports risk assessment. The proposed system provides a non-invasive, cost effective, and user-friendly solution for preliminary oral cancer screening, aiming to support healthcare professionals and improve early detection outcomes.

Keywords: deep learning, oral cancer detection, oral cancer, risk factors, cancer remedies, hospitals suggested.