基于多层次特征融合的口腔粘膜性疾病识别方法

Recognition of oral mucosal diseases based on multi-level feature fusion

口腔粘膜性疾病的识别主要依靠医生肉眼观察并进行主观判断,该方法导致疾病识别的准确率低、医生的工作量大。针对以上问题提出一种基于多层次特征融合的口腔粘膜性疾病识别方法。对口腔疾病图像提取深层次特征和浅层次特征共两种,使用EfficientNet模型做深层特征的提取,使用HSV、方向梯度直方图(histogram of oriented gradient,HOG)和灰度共生矩阵(gray-level co-occurrence matrix,GLCM)分别提取口腔疾病的颜色、形状以及纹理的浅层特征,将特征融合后利用随机森林(random forest,RF)算法进行特征选择,选取特征重要性更大的特征,降低特征的维度。最后结合多种机器学习分类器进行分类识别。使用收集到的口腔粘膜疾病数据集进行实验验证,实验结果表明,该方法能达到准确率(accuracy,Acc)92.89%、灵敏度(sensitivity,Sen)89.91%、特异性(specificity,Spe)96.06%以及AUC(area under the curve)98.09%,有效地解决识别中误判多、准确率低等问题。

The recognition of oral mucosal diseases mainly depends on doctors′visual observation and subjective judgment.This method leads to low accuracy of disease recognition and heavy workload of doctors.To solve the above problems,an oral mucosal disease recognition method based on multi-level feature fusion is proposed.There are two kinds of deep-level features and shallow features extracted from oral disease images.The efficientNet model is used to extract the deep features.HSV,histogram of oriented gradiant(HOG)and gray level co-occurrence matrix(GLCM)are used to extract the shallow features of color,shape and texture of oral diseases respectively.After feature fusion,the random forest(RF)algorithm is used to select the features with greater feature importance,reducing the dimension of the feature.Finally,a variety of machine learning classifiers are combined for classification and recognition.The datasets of oral mucoal diseases collected are used for experiment verification.The experimental results show that the method can achieve the accuracy(Acc)of 92.89%,sensitivity(Sen)of 89.91%,specificity(Spe)of 96.06%and area under the curve(AUC)of 98.09%.It effectively solves the problems of many misjudgments and low accuracy in recognition.