融合深度残差网络和字典学习的肺炎检测

由于空气污染与吸烟等原因, 肺炎已成为人类死亡率最高的疾病之一. 随着机器学习与深度学习技术在医疗图像检测上的应用, 为临床专家诊断各类疾病提供了帮助. 但由于缺少有效的配对肺部X射线数据集, 以及现有针对肺炎检测的方法均采用不是针对肺炎任务的普遍分类模型, 难以发现肺炎图像与正常图像的细微差别, 导致识别失败. 为此, 本文通过数据裁剪、旋转等方式扩充数据集中的正常图像; 再使用50层深度残差网络对胸部X射线中的浅层肺炎特征进行学习; 然后, 通过两层字典对残差网络学习到的肺炎特征进行更深度的抽象和学习, 发现不同肺部图像之间的微小差别; 最后, 融合残差网络和字典学习提取到的多级肺炎特征, 构建肺炎检测模型. 为了验证算法的有效性, 在Chest X-ray肺炎数据集上评估肺炎检测模型的性能. 根据测试结果, 本文提出模型的检测准确率为97.12%; 指标测试中, 精度与召回率之间的调和平均数上的得分为97.73%. 与现有方法相比, 获得了更高的识别精度.

Pneumonia Detection Based on Deep Residual Network and Dictionary Learning

Due to factors such as air pollution and smoking, pneumonia has become one of the diseases with the highest mortality rates in humans. The application of machine learning and deep learning technology in medical image detection has provided assistance for clinical experts in diagnosing various diseases. However, there is a lack of effective paired lung X-ray datasets, and existing methods for pneumonia detection use universal classification models that are not specific to pneumonia tasks. As a result, it is difficult to detect subtle differences between pneumonia images and normal images, resulting in recognition failure. Therefore, this study expands the normal images in the dataset through data cropping, rotation, and other methods and uses a 50-layer deep residual network to learn the shallow pneumonia features in chest X-rays. Then, through a two-layer dictionary, the pneumonia features learned by the residual network are further abstracted and learned, and subtle differences between different lung images are discovered. Finally, a pneumonia detection model is constructed by fusing the multi-level pneumonia features extracted from residual networks and dictionary learning. To validate the effectiveness of the algorithm, the performance of the pneumonia detection model is evaluated on the chest X-ray pneumonia dataset. According to the test results, the proposed model has a detection accuracy of 97.12%. In the indicator test, the score on the harmonic mean between accuracy and recall is 97.73%. Compared with existing methods, it has achieved higher recognition accuracy.