Oral Squamous Cell Detection Using Deep Learning
Abstract
Samrat Kumar Dev Sharma
Oral squamous cell carcinoma (OSCC) presents significant challenges due to increasing incidence rates and late diagnoses, despite advancements in understanding its molecular mechanisms. Early detection is vital for improving patient outcomes, and precision medicine offers personalized treatment options. Deep learning, particularly EfficientNetB3, shows promise in enhancing OSCC detection through automated image analysis. EfficientNetB3 demonstrated high performance in image classification, with an accuracy of 0.9833, precision of 0.9782, and recall of 0.9782. This article explores deep learning’s role in improving OSCC diagnosis, image analysis, and treatment planning, contributing to more timely interventions and better patient outcomes.