In-depth learning based image recognition method

Abstract

The invention discloses an in-depth learning based image recognition method and belongs to the technical field of machine learning. According to the invention, the method includes improving an in-depth learning model, namely, a VGG model, having good performance in an image processing field and replacing a full connection layer with a convolution layer; performing in-depth learning feature extraction on an image, training the extracted in-depth learning features through a SVM (Support Vector Machine) and finally performing classified identification. According to the invention, through identification and testification of the medical image, the method can effectively judge the type of the input medical input according to different classification standards; at the same time, the calculation cost can be reduced substantially and assistance can be provided to a doctor in disease diagnosis. By adopting the method provided by the invention, assistance can be provided to the doctor in diseasediagnosis in a objective perspective, diagnosis requirements of the doctor are met and diagnosis efficiency is improved, so that error diagnosis rate can be effectively reduced.

Description

technical field [0001] The technical field of the present invention is the field of image analysis, specifically an image recognition method based on deep learning. Background technique [0002] Machine learning methods are widely used in image analysis to complete specific tasks on new data, such as classification, recognition, and segmentation, by training a model on a given data set. Commonly used algorithms include support vector machine (SVM), hidden Markov (HMM) and artificial neural network. However, traditional machine learning algorithms need to use prior knowledge to manually extract features from raw data to train the model. Due to the difficulty of feature selection, the model may have overfitting problems, and the generalization ability is difficult to guarantee; on the other hand, traditional models are difficult to adapt to large-scale data sets, and the model scalability is poor. [0003] Deep learning is a new field in machine learning research, and its mo...

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Problems solved by technology

[0005] (1) It is necessary to manually extract features suitable for medical images, but extracting suitable features for different medical images takes a lot of time and requires a lot of experience in related fields

[0006] (2) In order to obtain a better deep learning model, a large amount of training data is required, and too small amount of data may lead to over-fitting of the model

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