Comparative Performance Analysis of Deep Learning Architectures for Oral Cancer Detection Using Histopathological Images
Early and accurate detection of oral cancer is critical for improving patient outcomes. This study presents a comprehensive comparison of five convolutional neural network architectures—MobileNetV2, Xception, VGG19, ResNet50, and DenseNet201—for automated oral cancer detection from histopathological images. All models were fine-tuned through transfer learning using ImageNet pretrained weights and evaluated on 5,192 histopathological images from Kaggle. On this dataset, MobileNetV2 achieved highest classification accuracy $(\mathbf{9 0. 0 0 \%})$, followed by DenseNet201 (87.69%), VGG19 (86.54%), Xception (86.15%), and ResNet50 (83.27%). Statistical validation via paired t-tests confirmed significant performance differences $(p<0.05)$. Beyond accuracy, MobileNetV2 demonstrated 38% faster training, 84% smaller model size, and 33% faster inference, showing promise for resource-constrained clinical settings pending external validation.