Automated Detection of Oral Squamous Cell Carcinoma Using Deep Learning

Oral Squamous Cell Carcinoma (OSCC) accounts for a significant proportion of oral malignancies and is often diagnosed at advanced stages due to limitations in early screening methods. To support timely and objective diagnosis, this study presents an automated deep learning–based framework for classifying histopathological oral tissue images as cancerous or non-cancerous. A Convolutional Neural Network (CNN) was trained using a publicly available dataset comprising 1,224 Haematoxylins and Eosin–stained images collected from 230 patients at two magnification levels (100× and 400×). Image preprocessing and class-aware data augmentation were applied to improve learning stability and reduce bias arising from class imbalance. The trained model achieved a training accuracy of 84% and a test accuracy of 77.61%, with high recall for cancerous samples, indicating effective sensitivity to malignant tissue patterns. Although a performance gap between training and validation results suggests moderate overfitting, the findings demonstrate that CNN-based analysis of histopathological images can serve as a reliable assistive tool for oral cancer screening. Future work will focus on enhancing generalization through larger, multi-center datasets and the evaluation of more advanced network architectures.