X-ray image classification method based on improved Resnet network
A classification method and optical image technology, applied in the field of image processing, can solve problems such as unbalanced distribution of data, achieve the effects of improving accuracy, improving overall performance, and enhancing extraction capabilities
Inactive Publication Date: 2021-10-08
CHONGQING UNIV OF POSTS & TELECOMM
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[0005] In view of this, the object of the present invention is to provide a method for classifying X-ray images based on an improved Resnet network, which solves the problems caused by the unbalanced distribution of chest X-ray original image data, and enhances the ability to extract feature information and improve the overall performance of the model , thereby improving the accuracy of chest X-ray image classification
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[0033] The embodiments of the present invention are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments are only used to illustrate the basic idea of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.
[0034] Among them, the accompanying drawings are only used for exemplary description, and represent only schematic diagrams, not physical drawings, and should not be ...
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The invention relates to an X-ray image classification method based on an improved Resnet network, and belongs to the technical field of image processing. According to the method, an X-ray image data set serves as image input, and training, testing and verification data sets of a model are obtained through an SMOTE data expansion algorithm; the classification model adopts a residual network ResNet as a basic framework of the model, and an extrusion excitation network is added into the residual network ResNet and is subjected to shortcut connection; horizontal convolution and vertical convolution are added into a common square convolution kernel in the model to form an asymmetric convolution structure, local key features are highlighted, and the training efficiency is improved; and a focus loss function is adopted, and the whole network can adjust the loss value of each disease according to the loss function so as to obtain a network model with higher disease classification capability. The X-ray image classification method can solve the problem caused by unbalanced data distribution, enhance the feature information extraction capability, and improve the overall performance of the model, thereby improving the accuracy of X-ray image classification.
Description
technical field [0001] The invention belongs to the technical field of image processing, and relates to an X-ray image classification method based on an improved Resnet network. Background technique [0002] Nowadays, it has become a routine step for doctors to assist doctors in diagnosing diseases by combining with medical imaging, but the existing medical imaging image analysis methods still have the defects of inaccurate analysis, such as: [0003] Wang et al. used a fully convolutional network pre-trained on ImageNet as a feature extractor in a weakly supervised manner and only trained transition and classification layers, and compared the classification and localization performance of four convolutional neural network models for lesion areas. , which effectively utilizes the ImageNet pre-training results, but uses the classical network to process multiple feature maps independently during training, ignoring the correlation between feature channels. Yao proposed a struc...
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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06T3/40G16H30/40
CPCG06N3/08G06T3/40G16H30/40G06T2207/20132G06T2207/10116G06T2207/20081G06T2207/20084G06T2207/10024G06N3/048G06N3/045G06F18/2415G06F18/241
Inventor 王慧倩毕瑞庞宇卢毅王元发罗家赛
Owner CHONGQING UNIV OF POSTS & TELECOMM