Comparative evaluation of CNN models for nasopharyngeal carcinoma classification on pathology data

This study presents a systematic evaluation of deep learning models for the classification of nasopharyngeal carcinoma (NPC) using whole slide images (WSIs) obtained from Sarawak General Hospital (SGH) and Hospital Kuala Lumpur (HKL). NPC, a malignancy with high prevalence in Southeast Asia, presents diagnostic challenges due to the histological similarities between normal and pathological tissues. A dataset of 88,002 images, annotated by expert pathologists and categorized into four classes: normal, lymphoid hyperplasia (LHP), nasopharyngeal inflammation (NPI), and NPC, was utilized for model training and evaluation. Several convolutional neural network (CNN) architectures, including DenseNet201, MobileNet, EfficientNetB0, InceptionNet, XceptionNet, VGG16, and NASNetMobile were systematically assessed alongside hybrid architectures formed through intermediate-level feature fusion of top-performing backbones. All models were evaluated using accuracy, precision, F1-score, and training time to ensure a balanced assessment of predictive performance and computational efficiency. Among individual models, MobileNet achieved the highest accuracy (96.9%), while DenseNet201 demonstrated the most balanced classification performance with the highest F1-score (94.9%). The hybrid EfficientNetB0 + DenseNet201 model achieved the overall best accuracy (97.6%), indicating that combining complementary feature representations can further enhance predictive capability. The integration of data augmentation and class weighting effectively mitigated dataset imbalance, resulting in substantial improvements in generalization and minority class recognition. Overall, the findings highlight the strong potential of optimized CNN architectures and feature-level fusion strategies for robust multi-class NPC classification, supporting their applicability in computer-aided diagnosis and assisting pathologists in improving diagnostic accuracy.