基于深度神经网络的肺炎图像识别模型

Pneumonia image recognition model based on deep neural network

当前的肺炎图像识别算法面临两个问题:一是肺炎特征提取器使用的迁移学习模型在源数据集与肺炎数据集上图像差异较大,所提取的特征不能很好地契合肺炎图像;二是算法使用的softmax分类器对高维特征处理能力不够强,在识别准确率上仍有提升的空间。针对这两个问题,提出了一种基于深度卷积神经网络的肺炎图像识别模型。首先使用ImageNet数据集训练好的GoogLeNet Inception V3网络模型进行特征提取;其次,增加了特征融合层,使用随机森林分类器进行分类预测。实验在Chest X-Ray Images肺炎标准数据集上进行。实验结果表明,该模型的识别准确率、敏感度、特异度的值分别达到96.77%、97.56%、94.26%。在识别准确率以及敏感度指标上,与经典的GoogLeNet Inception V3+Data Augmentation(GIV+DA)算法相比,所提模型分别提高了1.26、1.46个百分点,在特异度指标上已接近GIV+DA算法的最优结果。

Current recognition algorithm of pneumonia image faces two problems. First, the extracted features can not fit the pneumonia image well because the transfer learning model used by the pneumonia feature extractor has large image difference between the source dataset and the pneumonia dataset. Second, the softmax classifier used by the algorithm can not well process high-dimensional features, and there is still room for improvement in recognition accuracy. Aiming at these two problems, a pneumonia image recognition algorithm based on Deep Convolution Neural Network(DCNN) was proposed. Firstly, the GoogLeNet Inception V3 network model trained by ImageNet dataset was used to extract the features. Then, a feature fusion layer was added and random forest classifier was used to classify and forecast. Experiments were implemented on Chest X-Ray Images pneumonia standard dataset. The experimental results show that the recognition accuracy, sensitivity and specificity of the proposed model reach 96.77%, 97.56% and 94.26% respectively. The proposed model is 1.26 percentage points and 1.46 percentage points higher than the classic GoogLeNet Inception V3+Data Augmentation(GIV+DA) algorithm in the index of recognition accuracy and sensitivity, and is close to the optimal result of GIV+DA in the index of specificity.