Oral Cancer Detection Using Deep Learning
P. Kalaivani, Iyyanar Perumal, C. Rajan, R. Harshini Priya, P Janani, A S. Jayasudha · 2024
With a high death rate, oral cancers are widespread, complicated tumors. In addition to screening procedures, other methods for diagnosing oral cancer include biopsy, which entails removing a small sample of tissue from a part of the body and analyzing it under a microscope. By utilizing feature extraction and deep learning techniques, this study presents a strong diagnostic approach for the identification of oral cancer. For early diagnosis and categorization, modern technologies and a deep learning algorithm can be used. Employing the advanced EfficientNet architecture known for its efficacy in image processing tasks, our deep learning model capitalizes on this efficiency. The model is fine-tuned through transfer learning, utilizing pretrained weights to expedite convergence. This fusion of deep learning and feature extraction, particularly harnessing the capabilities of EfficientNet, showcases the potential of cutting edge technology in advancing oral cancer diagnostics. Utilizing these tools enables precise detection, enabling early intervention and potentially improving patient outcomes in the realm of healthcare.