Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient’s chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.
口腔癌是一种致命性疾病，在全球最常见癌症中位列第七。作为头颈部常见的恶性肿瘤类型，目前诊断的金标准是组织病理学检查，但传统方法耗时较长且需专业判读。因此，早期诊断口腔鳞状细胞癌（OSCC）对提高治疗成功率、降低死亡率和发病率、改善患者生存机会至关重要。本研究采用多种人工智能技术辅助临床医生，从而显著减轻病理学家的工作负担。研究旨在开发基于特征融合的混合方法，以提升OSCC早期诊断效能。 本研究采用三种不同策略，每种策略均使用五个独立模型。第一项策略采用迁移学习方法，运用Xception、Inceptionv3、InceptionResNetV2、NASNetLarge和DenseNet201模型。第二项策略使用预训练卷积神经网络进行特征提取，并结合支持向量机（SVM）进行分类。具体而言，通过Xception、Inceptionv3、InceptionResNetV2、NASNetLarge和DenseNet201等预训练模型提取特征，随后输入SVM算法评估分类准确率。最终策略采用前沿的混合特征融合技术，利用卷积神经网络模型提取上述模型的深度特征，通过主成分分析（PCA）进行降维处理，再将降维特征与灰度共生矩阵（GLCM）、方向梯度直方图（HOG）和局部二值模式（LBP）方法提取的形状、颜色及纹理特征相融合。将混合特征融合纳入SVM分类器以提升分类性能。 所提出的系统在组织学图像快速诊断OSCC方面取得显著成果。基于DenseNet201与GLCM、HOG、LBP特征融合的SVM算法，其准确率、精确率、灵敏度、特异度、F1分数和曲线下面积（AUC）分别达到97.00%、96.77%、90.90%、98.92%、93.74%和96.80%。
肿瘤(癌症)患者之家