Real Time Oral Cavity Detection Leading to Oral Cancer using CNN

Oral cancer presents a significant burden to global health, characterized by rising incidence rates and the associated mortality it entails. Timely identification and intervention hold immense potential in enhancing patient prognosis in the face of this disease. Nevertheless, conventional diagnostic approaches often hinge on subjective visual assessments by healthcare professionals, which can lead to potential delays in detection and diagnosis. The realm of medical image analysis has witnessed a transformative shift in recent times with the advent of deep learning methodologies. By harnessing the capabilities of convolutional neural networks (CNNs), scientists have made remarkable strides in employing these models to address diverse medical imaging tasks, including the early detection of cancerous conditions across different anatomical locations. In this paper, we propose a groundbreaking approach for the real-time identification of oral cavity conditions, with a specific emphasis on predicting the occurrence of oral cancer through the utilization of a deep learning framework. Our methodology synergistically integrates patient questionnaires and oral cavity images, culminating in a prediction model that enhances accuracy and reliability. Through this user-friendly system, individuals can undergo oral cancer diagnosis without invasive techniques, making early detection more accessible and potentially life-saving. The integration of communication with healthcare professionals further enhances the overall process, enabling users to seek timely guidance and consultation for better management of their oral health. This streamlined approach empowers individuals to actively participate in their healthcare, bridging the gap between medical technology and patient-centric care.