口腔癌是一种致命的疾病,在全球最常见的癌症中排名第七。口腔癌是一种通常影响头颈部的癌症。目前诊断的金标准是组织病理学检查,然而,常规方法耗时,需要专业解释。因此,口腔鳞状细胞癌(OSCC)的早期诊断对于成功治疗至关重要,降低死亡率和发病率的风险,同时提高患者的生存机会。因此,我们使用了几种人工智能技术来帮助临床医生或医生,从而大大减少病理学家的工作量。本研究旨在开发基于融合特征的混合方法,为OSCC的早期诊断提供更好的结果。这项研究采用了三种不同的策略,每个都使用五个不同的模型。第一种策略是使用Xception的迁移学习,Inceptionv3,InceptionResNetV2,NASNetLarge,和DenseNet201型号。第二种策略涉及使用CNN的预训练技术进行特征提取,并结合支持向量机(SVM)进行分类。特别是,使用各种预训练模型提取特征,即Xception,Inceptionv3,InceptionResNetV2,NASNetLarge,和DenseNet201,并随后应用于SVM算法以评估分类精度。最终策略采用了尖端的混合特征融合技术,利用CNN艺术模型来提取上述模型的深层特征。这些深层特征通过主成分分析(PCA)进行了降维。随后,低维特征与形状相结合,颜色,和使用灰度共生矩阵(GLCM)提取的纹理特征,方向梯度直方图(HOG),和局部二进制模式(LBP)方法。将混合特征融合结合到SVM中以增强分类性能。所提出的系统在使用组织学图像快速诊断OSCC方面取得了有希望的结果。准确性,精度,灵敏度,特异性,F-1得分,基于DenseNet201与GLCM混合特征融合的支持向量机(SVM)算法,HOG,LBP特征为97.00%,96.77%,90.90%,98.92%,93.74%,96.80%,分别。
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.