An Improved Multi-Scale Attention EfficientNet Framework for Oral Cancer Image Classification
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Automated oral cancer image classification has attracted increasing attention as deep learning offers opportunities to support diagnostic image analysis. However, limited dataset availability, redundant observations, and insufficient clinical metadata can compromise model reliability and the generalizability of reported performance. This study presents an improved Multi-Scale Attention EfficientNet (MSA-EfficientNet) framework that integrates hierarchical visual representations through attention-guided feature fusion. The proposed architecture extends EfficientNet-B0 by combining intermediate and deep feature maps, adaptive multi-scale weighting, and progressive transfer learning within a reproducible classification pipeline. The methodological framework incorporates duplicate-aware data partitioning, controlled augmentation, probability calibration, and model interpretability. An assessment of the Oral Cancer Multispectral Imaging Dataset (OC-MSI) identified 950 RGB images, comprising 500 cancer and 450 non-cancer observations, including 36 exact duplicates and leaving 914 unique images. The available archive lacks patient-level identifiers and independently accessible spectral channels, limiting the evaluation to RGB image classification. A completed preliminary DARC-Net experiment achieved 63.77% classification accuracy, 69.01% sensitivity, 58.21% specificity, and an area under the receiver operating characteristic curve of 0.6523. A separate simulation-based analysis reported 93.48% accuracy and an ROC–AUC of 0.975, alongside a structured 30-epoch optimization profile; these values are not measured outcomes of MSA-EfficientNet. The study establishes a technically specified architectural framework and comprehensive evaluation strategy for investigating attention-enhanced EfficientNet classification. Experimental implementation, matched-baseline comparisons, ablation analysis, and independent validation remain necessary to determine the model's actual predictive effectiveness and potential clinical relevance.
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