Image recognition method combining convolutional neural network and gradient lifting tree

Abstract

The invention discloses an image recognition method combining a convolutional neural network and a gradient lifting tree, and belongs to the technical field of mechanical learning. According to the method, a group of image patches are obtained according to the size of an input image. The image patches and an original image serve as input data together. Five branches are adopted. Each branch employs an improved VGG-19 model to extract features of an input image. Then, features are inputted to a gradient boosting tree for training to acquire a basic classifer. A weighted voting method is used for combining the basic classifier into a strong classifier to be used for classifying the input image. According to the method, medical images are identified and proved, the input medical images can beaccurately and rapidly classified, doctors are assisted to diagnose diseases, the diagnosis efficiency is improved, and therefore the misdiagnosis rate is effectively reduced.

Description

technical field [0001] The invention relates to an image recognition method combining a convolutional neural network and a gradient boosting tree, belonging to the field of image analysis. Background technique [0002] In this information age, image analysis is indispensable in our daily life, and the use of machine learning methods can help people manage images efficiently. Establish a machine learning model, and the training model obtained by training on a given data set can complete some specific tasks of new image data, such as recognition, classification and segmentation. As a traditional machine learning algorithm, support vector machine has a remarkable effect in the field of image analysis. However, the accuracy of traditional machine learning algorithms relies on prior knowledge to extract features through artificially designed algorithms to train the model. Due to the difficulty of manually selecting features, the model is prone to underfitting and overfitting; D...

AI Technical Summary

Problems solved by technology

[0006] (1) Traditional machine learning is difficult to manually extract features, it is difficult to adapt to large-scale data training, and the generalization ability of the model is poor

[0007] (2) The pure deep learning model requires a large-scale data set to train the neural network, but currently there is no such large-scale medical image data set, and it is more difficult to obtain some medical image data containing private information, training the deep learning model It is easy to overfit when

[0008] (3) The accuracy of existing deep learning and traditional machine learning combined models needs to be improved

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