Significance: Oral cancer is a quite common global health issue. Early diagnosis of cancerous and potentially malignant disorders in the oral cavity would significantly increase the survival rate of oral cancer. Previously reported smartphone-based images detection methods for oral cancer mainly focus on demonstrating the effectiveness of their methodology, yet it still lacks systematic study on how to improve the diagnosis accuracy on oral disease using hand-held smartphone photographic images. Aim: We present an effective smartphone-based imaging diagnosis method, powered by a deep learning algorithm, to address the challenges of automatic detection of oral diseases. Approach: We conducted a retrospective study. First, a simple yet effective centered rule image-capturing approach was proposed for collecting oral cavity images. Then, based on this method, a medium-sized oral dataset with five categories of diseases was created, and a resampling method was presented to alleviate the effect of image variability from hand-held smartphone cameras. Finally, a recent deep learning network (HRNet) was introduced to evaluate the performance of our method for oral cancer detection. Results: The performance of the proposed method achieved a sensitivity of 83.0%, specificity of 96.6%, precision of 84.3%, and F1 of 83.6% on 455 test images. The proposed “center positioning” method was about 8% higher than that of a simulated “random positioning” method in terms of F1 score, the resampling method had additional 6% of performance improvement, and the introduced HRNet achieved slightly better performance than VGG16, ResNet50, and DenseNet169, with respect to the metrics of sensitivity, specificity, precision, and F1. Conclusions: Capturing oral images centered on the lesion, resampling the cases in training set, and using the HRNet can effectively improve the performance of deep learning algorithm on oral cancer detection. The smartphone-based imaging with deep learning method has good potential for primary oral cancer diagnosis.

中文翻译：

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使用深度学习进行早期诊断，自动检测基于智能手机的图像中的口腔癌

意义：口腔癌是一个相当普遍的全球健康问题。口腔癌性和潜在恶性疾病的早期诊断将显着提高口腔癌的存活率。此前报道的基于智能手机的口腔癌图像检测方法主要侧重于证明其方法的有效性，但对于如何利用手持智能手机摄影图像提高口腔疾病的诊断准确性仍缺乏系统研究。目的：我们提出了一种有效的基于智能手机的成像诊断方法，由深度学习算法提供支持，以解决口腔疾病自动检测的挑战。方法：我们进行了一项回顾性研究。首先，提出了一种简单而有效的居中规则图像捕获方法来收集口腔图像。然后，基于这种方法，创建了一个包含五类疾病的中等规模的口腔数据集，并提出了一种重采样方法来减轻手持智能手机相机的图像可变性的影响。最后，引入了最近的深度学习网络 (HRNet) 来评估我们的口腔癌检测方法的性能。结果：所提方法的性能在455张测试图像上实现了83.0%的灵敏度、96.6%的特异性、84.3%的精确度和83.6%的F1。提出的“中心定位”方法在 F1 分数方面比模拟的“随机定位”方法高约 8%，重采样方法额外提高了 6% 的性能，引入的 HRNet 实现了略优于 VGG16 的性能， ResNet50 和 DenseNet169，关于敏感性指标，特异性、精密度和 F1。结论：以病灶为中心采集口腔图像，对训练集中的病例进行重采样，并使用HRNet可以有效提高深度学习算法在口腔癌检测中的性能。具有深度学习方法的基于智能手机的成像具有用于原发性口腔癌诊断的良好潜力。