Attention mechanism-based in-depth learning diabetic retinopathy classification method

Abstract

The invention discloses an attention mechanism-based in-depth learning diabetic retinopathy classification method comprising the following steps: a series of eye ground images are chosen as original data samples which are then subjected to normalization preprocessing operation, the preprocessed original data samples are divided into a training set and a testing set after being cut, a main neutralnetwork is subjected to parameter initializing and fine tuning operation, images of the training set are input into the main neutral network and then are trained, and a characteristic graph is generated; parameters of the main neutral network are fixed, the images of the training set are adopted for training an attention network, pathology candidate zone degree graphs are output and normalized, anattention graph is obtained, an attention mechanism is obtained after the attention graph is multiplied by the characteristic graph, an obtained result of the attention mechanism is input into the main neutral network, the images of the training set are adopted for training operation, and finally a diabetic retinopathy grade classification model is obtained. Via the method disclosed in the invention, the attention mechanism is introduced, a diabetic retinopathy zone data set is used for training the same, and information characteristics of a retinopathy zone is enhanced while original networkcharacteristics are reserved.

Description

technical field [0001] The invention relates to a deep learning diabetic retinopathy classification method based on an attention mechanism, and belongs to the field of medical image processing. Background technique [0002] At present, clinical doctors diagnose diabetic retinopathy by observing and analyzing early pathological features on retinal fundus images, such as microaneurysms, hard exudates, and hemorrhages. In practice, there are many types of diabetic retinopathy, various lesions, and varying degrees of severity, making it difficult for ophthalmologists to diagnose. Therefore, in large-scale screening of diabetic retinopathy, computer-aided diagnosis technology can greatly reduce the burden on doctors, and quickly and effectively assist doctors to achieve classified diagnosis. [0003] In most of the current automatic diagnosis algorithms, the classification of fundus images of diabetic retinopathy is mainly based on traditional manual methods to design and extrac...

AI Technical Summary

Problems solved by technology

For example, using manual features including shape, color, brightness and prior knowledge for diabetic retinopathy detection, these methods can only achieve better results on small data sets, due to the cumbersome manual feature extraction process, in large data sets Inefficient and less robust in the case of

With the development of artificial intelligence algorithms, some researchers have proposed a method for the classification and diagnosis of diabetic retinopathy directly based on deep learning. For example, the convolutional neural network is directly connected to fundus images for the classification task of diabetic retinopathy. Designed for all types of diabetic retinopathy, only regards the convolutional neural network as a black box, does not take into account the lesion distribution information closely related to the diagnosis, and lacks an effective and intuitive explanation

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