Development of AI Powered Model for Oralcancer Detection

Oral cancer detection, a critical health challenge due to late-stage diagnoses, has seen significant advancements through the integration of both hardware and software technologies. The system, designed for the early detection of oral cancer, combines innovative hardware, such as robotic arms for assistive feeding, cost-effective control systems for industrial robotics using Raspberry Pi, and Io T - enabled devices for remote oral examinations, with advanced software solutions. These hardware systems utilize lightweight structures, motorized joints, stepper motors and precise motion control to enhance functionality in healthcare and industrial environments. For early detection, Machine Learning (ML) and Deep Learning (DL) techniques, including Convolutional Neural Networks (CNNs) and transfer learning models like VGG19 and InceptionNetV3, Res-Net and Dense-Net, have been employed to analyse medical images. Image processing methods such as GLCM, wavelet transforms, and CNN-based feature extraction improve classification accuracy, while optimization techniques like Fuzzy Particle Swarm Optimization (FPSO) and Artificial Bee Colony (ABC) enhance performance. The integration of these systems achieves high sensitivity, specificity, and precision, making them effective in early oral cancer detection. By combining advanced diagnostic technologies, the system offers a comprehensive approach that improves early diagnosis, enhances patient outcomes, and complements traditional methods.