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Abstract：
Oral lesions are a prevalent manifestation of oral disease, and the timely identification of oral lesions is imperative for effective intervention. Fortunately, deep learning algorithms have shown great potential for automated lesion detection. The primary aim of this study was to employ deep learning-based image classification algorithms to identify oral lesions. We used three deep learning models, namely VGG19, DeIT, and MobileNet, to assess the efficacy of various categorization methods. To evaluate the accuracy and reliability of the models, we employed a dataset consisting of oral pictures encompassing two distinct categories: benign and malignant lesions. The experimental findings indicate that VGG19 and MobileNet attained an almost perfect accuracy rate of 100%, while DeIT achieved a slightly lower accuracy rate of 98.73%. The results of this study indicate that deep learning algorithms for picture classification demonstrate a high level of effectiveness in detecting oral lesions by achieving 100% for VGG19 and MobileNet and 98.73% for DeIT. Specifically, the VGG19 and MobileNet models exhibit notable suitability for this particular task.
摘要：
口腔病变是口腔疾病的普遍表现,及时识别口腔病变对有效干预势在必行。幸运的是,深度学习算法已显示出自动化病变检测的巨大潜力。这项研究的主要目的是采用基于深度学习的图像分类算法来识别口腔病变。我们使用了三种深度学习模型，即VGG19，DeIT，和MobileNet，评估各种分类方法的有效性。为了评估模型的准确性和可靠性，我们使用了一个由口腔图片组成的数据集，其中包含两个不同的类别：良性和恶性病变。实验结果表明，VGG19和MobileNet几乎达到了100%的完美准确率，而DeIT的准确率略低，为98.73%。这项研究的结果表明，用于图片分类的深度学习算法在检测口腔病变方面表现出很高的有效性，VGG19和MobileNet达到100%，DeIT达到98.73%。具体来说,VGG19和MobileNet模型对这一特定任务表现出显著的适用性。
