RSANet: An Advance in Oral Cancer Prediction using Residual Network with Soft-Attention Mechanism

Oral cancer is still a major worldwide health problem, especially in South and Southeast Asia, with late detection and variability in diagnosis leading to poor survival. Histopathological investigation is the gold standard of diagnosis but is time-consuming, subjective and pathologist dependant. We propose RSANet in this work, a deep learning framework integrating a ResNet50 backbone with a soft-attention mechanism for discriminating normal tissue from oral squamous cell cancer (OSCC) in histological pictures. The residual backbone encodes hierarchical morphological patterns, while the attention mechanism distributes higher weights to selected spatial regions and provides visual maps to check model focus. RSANet obtained an accuracy of 97.56%, a sensitivity of 98.33% and an F1-score of 97.75% on the held-out test set. The ResNet50 baseline acquired an accuracy of 91.40%, for comparison, equal to an absolute gain of 6.16 percentage points. Our results show that using soft attention improves classification performance in the experimental setup we analyzed. RSANet is a promising computer-assisted OSCC classification system that might support pathologists in making diagnostic decisions, however independent validation in multicenter setting is needed prior to its possible clinical use.