DSR-VGG: A Lightweight Depthwise Separable Residual VGG Architecture for Nasopharyngeal Histopathological Image Classification

Background/Objectives: Deep convolutional neural networks achieve strong histopathological image-classification performance but often require substantial computational resources. This study proposes DSR-VGG, a lightweight Depthwise Separable Residual VGG architecture for nasopharyngeal histopathological image classification. Methods: DSR-VGG comprises six Depthwise Separable Blocks with residual projection shortcuts, global average pooling, dropout regularization, and a classification layer. An inverse-frequency weighted random sampling strategy was used to address severe class imbalance. The primary evaluation used 88,002 nasopharyngeal histopathology patches from four classes: Normal, lymphoid hyperplasia, nasopharyngeal inflammation, and nasopharyngeal carcinoma. Training was repeated using three random seeds, with supplementary public datasets used to assess cross-dataset architectural robustness. Results: Across three runs, DSR-VGG achieved 99.764 ± 0.049% accuracy, 99.070 ± 0.365% balanced accuracy, and 99.219 ± 0.215% macro-F1. The model contained only 0.528 million parameters, required approximately 0.31 GFLOPs, and achieved 2.38 ms single-image inference latency under the stated GPU benchmarking conditions. Although several baseline architectures achieved marginally higher accuracy, DSR-VGG maintained competitive predictive performance with substantially lower computational complexity. Conclusions: DSR-VGG provides a favorable accuracy–complexity trade-off for histopathological patch classification. However, patient-/WSI-disjoint evaluation, independent multi-center validation, and whole-slide assessment remain necessary before clinical deployment.