Smartphone-Assisted Deep Learning Model For Oral Cancer Screening At Rural PHCs

Authors: Rubina Begam M, Manikandan Sathyamoorthy, Mohamed Akram. M, Mohamed Faizal. B, Magudesh. M

Abstract: Oral cancer is a significant public health concern, especially in rural areas where access to specialized diagnostic facilities is limited. Early detection is crucial for improving survival rates and reducing treatment costs. This paper presents a smartphone-assisted deep learning model for oral cancer screening at Rural Primary Health Centers (PHCs). The proposed system utilizes oral lesion images captured using smartphone cameras and applies deep learning techniques for automated classification of normal and suspicious cancerous lesions. Image preprocessing methods are employed to enhance quality and improve feature extraction, while a Convolutional Neural Network (CNN) model is trained to achieve accurate lesion detection. The integration of smartphone technology with artificial intelligence enables a low-cost, portable, and user-friendly screening solution suitable for resource-limited settings. The developed model assists healthcare workers in performing preliminary screening and supports timely referral for further diagnosis and treatment. Experimental results demonstrate promising accuracy and reliability in detecting oral abnormalities, showing the potential of the proposed system as an effective screening tool. This approach bridges the gap between advanced diagnostic technologies and rural healthcare delivery, contributing to early detection, improved accessibility, and reduction in oral cancer burden.

DOI: https://doi.org/10.5281/zenodo.19759303