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India, the world’s second-largest consumer of tobacco, faces a significant public health threat from oral cancer (OC), that mostly spotted at late stages, leading to a five-year survival rate of just 20%. This study investigates the potential of Artificial Intelligence (AI) and deep learning, particularly Convolutional Neural Networks (CNNs), for the non-invasive early detection of OC and Potentially Malignant Disorders (OPMDs). Using white light imaging, we evaluate the performance of pre-trained deep learning models, including ResNet-50, VGG-16, VGG-19, and Xception, in accurately classifying suspicious oral lesions. The results demonstrate the efficacy of AI-driven approaches in enhancing diagnostic accuracy, offering a promising tool for early screening and timely intervention.

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Early Detection of Oral Cancer: An Approach Using Deep Convolution Neural Network with Transfer Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Pravesh,
  • Vidhi Bishnoi,
  • Vibhav Kumar Sachan,
  • Richa Srivastava

摘要

India, the world’s second-largest consumer of tobacco, faces a significant public health threat from oral cancer (OC), that mostly spotted at late stages, leading to a five-year survival rate of just 20%. This study investigates the potential of Artificial Intelligence (AI) and deep learning, particularly Convolutional Neural Networks (CNNs), for the non-invasive early detection of OC and Potentially Malignant Disorders (OPMDs). Using white light imaging, we evaluate the performance of pre-trained deep learning models, including ResNet-50, VGG-16, VGG-19, and Xception, in accurately classifying suspicious oral lesions. The results demonstrate the efficacy of AI-driven approaches in enhancing diagnostic accuracy, offering a promising tool for early screening and timely intervention.