A Smartphone-based Deep Learning Framework for Early Detection of Oral Cancer Signs

Oral cancer can become non-fatal if promptly detected and treated with medication. However, failure to diagnose cancer at an early stage poses a significant risk to lives. Therefore, early detection of oral cancer plays a vital role in preserving lives. Recently, there has been an increase in the use of deep learning (DL) algorithms for early disease diagnosis. We introduce a highly effective method for diagnosing pre-cancerous lesions in the oral cavity using smartphone-based deep learning (DL) framework. This method holds the promise of decreasing illness, death rates, and the overall expenses associated with healthcare. Initially, the oral lesion images were captured using a hand-held smartphone. Then, the lesion samples were annotated by skilled oral pathologists who delineated bounding boxes around the affected areas. This annotation process utilized the Visual Geometry Group (VGG) image annotator tool and led to the creation of an oral lesion dataset encompassing four distinct classes. For lesion detection, You Only Look Once (YOLOv8) is employed, while image classification is carried out using ConvNextBase architecture. The proposed method achieves impressive performance with an accuracy of 87.89%. Preliminary results showcase the feasibility of our real-time automated approach for detecting and classifying oral lesions. With its low-cost and non-invasive nature, The proposed framework has a lot of potential as an useful tool to aid the screening process and improve oral cancer detection.