Image-Based Screening of Oral Cancer via Deep Ensemble Architecture

Abstract

Oral squamous cell carcinoma (OSCC) is a significant health issue in the oral cancer domain; a screening tool for timely and accurate diagnosis is essential for effective treatment planning and prognosis in patients' life expectancy. In this paper, we address the problem of object detection and classification in the context of OSCC, by presenting a comparative analysis of three state-of-the-art architecture: YOLO, FasterRCNN, and DETR. We propose a deep learning ensemble model to address both object detection and classification problem leveraging the strengths of individual models to achieve higher performance than single models. The proposed architecture was evaluated on a real-world dataset developed by experienced clinicians who manually labeled individual photographic images, producing a benchmark dataset. Results from our comparative analysis demonstrates the ensemble detection model achieves superior performance compared to the individual models, outperforming the average value of the individual models' map@50 metric by 24% and the value of the map@95-50 metric by 44%.