Song Simin, Ren Xiaojing, He Jing, Gao Meng, Wang Jia'nan, Wang Bin
The Second Medical Center, Chinese People's Liberation Army General Hospital, Beijing 100089, China.
The First Medical Center, Chinese People's Liberation Army General Hospital, Beijing 100853, China.
Diagnostics (Basel). 2023 Jul 24;13(14):2454. doi: 10.3390/diagnostics13142454.
Oral cancer is introduced as the uncontrolled cells' growth that causes destruction and damage to nearby tissues. This occurs when a sore or lump grows in the mouth that does not disappear. Cancers of the cheeks, lips, floor of the mouth, tongue, sinuses, hard and soft palate, and lungs (throat) are types of this cancer that will be deadly if not detected and cured in the beginning stages. The present study proposes a new pipeline procedure for providing an efficient diagnosis system for oral cancer images. In this procedure, after preprocessing and segmenting the area of interest of the inputted images, the useful characteristics are achieved. Then, some number of useful features are selected, and the others are removed to simplify the method complexity. Finally, the selected features move into a support vector machine (SVM) to classify the images by selected characteristics. The feature selection and classification steps are optimized by an amended version of the competitive search optimizer. The technique is finally implemented on the Oral Cancer (Lips and Tongue) images (OCI) dataset, and its achievements are confirmed by the comparison of it with some other latest techniques, which are weight balancing, a support vector machine, a gray-level co-occurrence matrix (GLCM), the deep method, transfer learning, mobile microscopy, and quadratic discriminant analysis. The simulation results were authenticated by four indicators and indicated the suggested method's efficiency in relation to the others in diagnosing the oral cancer cases.
口腔癌是指细胞不受控制地生长，对附近组织造成破坏和损伤。当口腔中出现疼痛或肿块且不消失时，就会发生这种情况。脸颊、嘴唇、口腔底部、舌头、鼻窦、硬腭和软腭以及肺部（喉咙）的癌症都属于这种癌症类型，如果在早期阶段未被发现和治愈，将会致命。本研究提出了一种新的流水线程序，用于为口腔癌图像提供高效的诊断系统。在这个程序中，对输入图像的感兴趣区域进行预处理和分割后，获得有用的特征。然后，选择一些有用的特征，去除其他特征以简化方法的复杂性。最后，将所选特征输入支持向量机（SVM），通过所选特征对图像进行分类。特征选择和分类步骤通过竞争搜索优化器的改进版本进行优化。该技术最终在口腔癌（嘴唇和舌头）图像（OCI）数据集上实现，通过与其他一些最新技术（如权重平衡、支持向量机、灰度共生矩阵（GLCM）、深度方法、迁移学习、移动显微镜和二次判别分析）进行比较，证实了其成果。模拟结果通过四个指标得到验证，表明所提出的方法在诊断口腔癌病例方面相对于其他方法具有效率。
