EARLY DETECTION AND CLASSIFICATION OF ORAL LESIONS USING DEEP LEARNING

Deep Learning

Oral cancer poses a significant global health challenge, with India bearing a disproportionate burden, evidenced by over 48,000 annual deaths attributed to the disease. Addressing this issue requires innovative approaches to improve early detection and classification of oral lesions, ultimately enhancing patient outcomes and reducing mortality rates. This project aims to revolutionize oral healthcare by leveraging deep learning techniques to promptly detect and classify oral lesions. Early detection of oral lesions is critical for timely intervention and improved prognosis, particularly in cases of potential malignancy. To achieve this, we utilize a comprehensive dataset comprising diverse oral images, including normal cases, lesions, and malignant conditions. Our deep learning model, based on Convolutional Neural Networks (CNN), is trained and fine-tuned using both SqueezeNet and ConvNet architectures. Through rigorous data augmentation and intensive training, the CNN model learns to accurately identify and categorize oral lesions, while also estimating the probability of tumor presence within a given dataset, thus aiding in lesion type determination. By automating the identification of potentially malignant oral lesions, our project aims to enable cost-effective and early diagnosis of oral cancer. We envision the development of a user-friendly interface that empowers clinicians to input patient data and oral images, facilitating real-time feedback and streamlining the oral lesion diagnosis process. Our approach holds promise for enhancing oral healthcare delivery, potentially saving lives through timely intervention and improved diagnostic accuracy.

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