Abstract
Aims Late diagnosis of Oral Squamous Cell Carcinoma (OSCC) contributes significantly to its high global mortality rate, with over 50\% of cases detected at advanced stages and a 5-year survival rate below 50\% according to WHO statistics. This study aims to improve early detection of OSCC by developing a multimodal deep learning framework that integrates clinical, radiological, and histopathological images using a weighted ensemble of DenseNet-121 convolutional neural networks (CNNs). Material and Methods A retrospective study was conducted using publicly available datasets representing three distinct medical imaging modalities. Each modality-specific dataset was used to train a DenseNet-121 CNN via transfer learning. Augmentation and modality-specific preprocessing were applied to increase robustness. Predictions were fused using a validation-weighted ensemble strategy. Evaluation was performed using accuracy, precision, recall, F1-score. Results High validation accuracy was achieved for radiological (100\%) and histopathological (95.12\%) modalities, with clinical images performing lower (63.10\%) due to visual heterogeneity. The ensemble model demonstrated improved diagnostic robustness with an overall accuracy of 84.58\% on a multimodal validation dataset of 55 samples. Conclusion The multimodal ensemble framework bridges gaps in the current diagnostic workflow by offering a non-invasive, AI-assisted triage tool that enhances early identification of high-risk lesions. It supports clinicians in decision-making, aligning with global oncology guidelines to reduce diagnostic delays and improve patient outcomes.
Abstract (translated)
口腔鳞状细胞癌（OSCC）晚期诊断率高是其全球死亡率高的主要原因，据世界卫生组织统计，超过50%的病例在晚期被发现，五年生存率低于50%。本研究旨在通过开发一种多模态深度学习框架来提高OSCC早期检测能力，该框架整合了临床、放射学和病理图像，并使用DenseNet-121卷积神经网络（CNN）的加权集成。 **材料与方法** 进行了一项回顾性研究，利用公开可用的数据集代表三种不同的医学影像模态。每个特定模态的数据集通过迁移学习训练了一个DenseNet-121 CNN。为了提高鲁棒性，应用了数据增强和模态特异性预处理。预测结果采用验证加权集成策略进行融合。使用准确性、精确度、召回率、F1分数进行评估。 **结果** 放射学（100%）和病理图像（95.12%）的高验证准确率得到了实现，而临床图像的表现较低（63.10%），这是由于视觉异质性导致的。整个多模态验证数据集包含55个样本时，集成模型在诊断鲁棒性方面有所提高，总体准确率为84.58%。 **结论** 该多模态集成框架通过提供一种非侵入性的、人工智能辅助的初步筛查工具来弥合当前诊断工作流程中的差距，有助于早期识别高风险病灶。它支持临床医生决策，并符合全球肿瘤学指南，减少诊断延迟，改善患者预后。
URL
https://arxiv.org/abs/2510.03878
