Early detection of oral cancer and potentially malignant disorders is challenging in low-resource settings due to limited annotated data. We present a unified four-class oral lesion classifier that integrates deep RGB embeddings, hyperspectral reconstruction, handcrafted spectral-textural descriptors, and demographic metadata. A pathologist-verified subset of oral cavity images was curated and processed using a fine-tuned ConvNeXt-v2 encoder, followed by RGB-to-HSI reconstruction into 31-band hyperspectral cubes. Haemoglobin-sensitive indices, texture features, and spectral-shape measures were extracted and fused with deep and clinical features. Multiple machine-learning models were assessed with patient-wise validation. We further introduce an incremental heuristic meta-learner (IHML) that combines calibrated base classifiers through probabilistic stacking and patient-level posterior smoothing. On an unseen patient split, the proposed framework achieved a macro F1 of 66.23% and an accuracy of 64.56%. Results demonstrate that hyperspectral reconstruction and uncertainty-aware meta-learning substantially improve robustness for real-world oral lesion screening.

翻译：在低资源环境下，由于标注数据有限，口腔癌及潜在恶性病变的早期检测面临挑战。本文提出了一种统一的四类口腔病变分类器，该分类器整合了深度RGB嵌入、高光谱重建、手工设计的谱-纹理描述符以及人口统计学元数据。通过使用微调后的ConvNeXt-v2编码器对经病理学家验证的口腔图像子集进行整理和处理，随后将RGB图像重建为31波段的高光谱立方体。提取了对血红蛋白敏感的指数、纹理特征及光谱-形状度量，并将其与深度特征及临床特征进行融合。采用基于患者的分层验证策略评估了多种机器学习模型。我们进一步提出了一种增量启发式元学习器（IHML），它通过概率堆叠和患者级后验平滑来组合校准后的基分类器。在未见患者数据划分上，所提框架实现了66.23%的宏F1分数和64.56%的准确率。结果表明，高光谱重建与不确定性感知的元学习显著提升了实际口腔病变筛查的鲁棒性。