Zafar Amad, Khalid Majdi, Farrash Majed, Qadah Thamir M, Lahza Hassan Fareed M, Kim Seong-Han
Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea.
Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah 24382, Saudi Arabia.
Bioengineering (Basel). 2024 Sep 12;11(9):913. doi: 10.3390/bioengineering11090913.
Oral cancer, also known as oral squamous cell carcinoma (OSCC), is one of the most prevalent types of cancer and caused 177,757 deaths worldwide in 2020, as reported by the World Health Organization. Early detection and identification of OSCC are highly correlated with survival rates. Therefore, this study presents an automatic image-processing-based machine learning approach for OSCC detection. Histopathological images were used to compute deep features using various pretrained models. Based on the classification performance, the best features (ResNet-101 and EfficientNet-b0) were merged using the canonical correlation feature fusion approach, resulting in an enhanced classification performance. Additionally, the binary-improved Haris Hawks optimization (b-IHHO) algorithm was used to eliminate redundant features and further enhance the classification performance, leading to a high classification rate of 97.78% for OSCC. The b-IHHO trained the k-nearest neighbors model with an average feature vector size of only 899. A comparison with other wrapper-based feature selection approaches showed that the b-IHHO results were statistically more stable, reliable, and significant ( < 0.01). Moreover, comparisons with those other state-of-the-art (SOTA) approaches indicated that the b-IHHO model offered better results, suggesting that the proposed framework may be applicable in clinical settings to aid doctors in OSCC detection.
口腔癌，也称为口腔鳞状细胞癌（OSCC），是最常见的癌症类型之一。据世界卫生组织报告，2020年全球有177,757人死于口腔癌。OSCC的早期检测和识别与生存率高度相关。因此，本研究提出了一种基于自动图像处理的机器学习方法用于OSCC检测。使用各种预训练模型对组织病理学图像进行深度特征计算。基于分类性能，使用典型相关特征融合方法合并最佳特征（ResNet-101和EfficientNet-b0），从而提高分类性能。此外，使用二进制改进的哈里斯鹰优化（b-IHHO）算法消除冗余特征并进一步提高分类性能，OSCC的分类率高达97.78%。b-IHHO训练的k近邻模型平均特征向量大小仅为899。与其他基于包装器的特征选择方法的比较表明，b-IHHO的结果在统计学上更稳定、可靠且具有显著性（<0.01）。此外，与其他最新技术（SOTA）方法的比较表明，b-IHHO模型提供了更好的结果，这表明所提出的框架可能适用于临床环境，以帮助医生进行OSCC检测。
