Diabetic retinopathy image classification method based on improved ResNeSt convolutional neural network model

Abstract

The invention discloses a diabetic retinopathy image classification method based on an improved ResNeSt convolutional neural network model. The method comprises the following steps: acquiring a lesion image from a hospital; preprocessing the image, manually marking the image by an ophthalmologist, and dividing a data set; establishing a deep learning server platform required by the experiment then, and then writing a python code; introducing two lightweight and efficient convolution operations of OctConv and SPConv into a ResNeSt convolutional neural network, and introducing a learning rate mediation mechanism of Warm Restart and cosine annealing; pre-training the improved ResNeSt network by adopting an ILSVRC2012 data set, and migrating the obtained model to the preprocessed data set for fine tuning; and loading a test set, testing the trained ResNeSt convolutional neural network classification model to obtain a classification result, and judging whether each classification index meets the requirement or not. The diabetic retinopathy image classification method is realized, the improved ResNeSt model is utilized, the operation efficiency and the classification accuracy are high, and the application value is very high.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a diabetic retinopathy image classification method based on an improved ResNeSt convolutional neural network model. Background technique [0002] Diabetes is a body metabolic disease with a relatively high incidence rate, mostly in older groups. It will not only have a serious impact on the normal blood sugar metabolism of the human body, but also cause damage to other parts of the body. Among them, diabetic retinopathy ( DR) is a typical complication of diabetes. DR will not only have a great impact on people's vision, but also cause damage to the brain nerves. DR generally has a latent period of 10-15 years from early stage to blindness, and the early stage is not prominent, so it often does not attract people's attention. [0003] Diabetic retinopathy not only causes blindness to patients, but also causes serious mental and economic burdens to pa...

AI Technical Summary

Problems solved by technology

The traditional detection of diabetic retinopathy mainly relies on professional ophthalmologists for manual identification, but this process is time-consuming, labor-intensive and inefficient, and may even easily lead to misdiagnosis

In addition, there is currently a serious shortage of ophthalmologists in China, and the level of diagnosis is uneven

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