Detection method for eyeground multi-disease classification based on deep learning

A deep learning and detection method technology, applied in the field of medical image processing, can solve problems such as low detection efficiency and complex models, and achieve the effects of high precision, fast speed, improved efficiency and accuracy

Pending Publication Date: 2020-11-17
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

In the existing multi-disease diagnosis methods, there are al

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  • Detection method for eyeground multi-disease classification based on deep learning
  • Detection method for eyeground multi-disease classification based on deep learning
  • Detection method for eyeground multi-disease classification based on deep learning

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Embodiment 1

[0027] Implementation Example 1: The detection method for the classification of multiple fundus diseases based on deep learning provided by the present invention firstly performs preprocessing and data enhancement on the input fundus image to improve the robustness of the network, and then performs feature extraction and Attention Module through EfficientNet Carry out feature enhancement, use the fully connected layer for classification judgment, and finally output the classification probabilities, such as figure 1 As shown, the detection method of fundus multi-disease classification based on deep learning includes the following steps:

[0028] Step 1: Select a dataset. The data sets used in the present invention are selected from a total of 36,910 fundus color photo data provided by Nanjing Mingji Hospital, Jiangsu Provincial Government Hospital and Jiangsu Provincial People's Hospital. The data of each disease are: normal: 17152; high myopia: 7475; other abnormalities: 3990...

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Abstract

The invention discloses a detection method for eyeground multi-disease classification based on deep learning. The method comprises the steps of obtaining an eyeground image, and selecting a training set and a test set according to independent identically-distributed conditions; preprocessing and enhancing the eyeground image, and extracting eyeground color photo image features; establishing a convolutional neural network model based on an EffecientNet + Attention Module, and adjusting the network structure; initializing network parameters by using a migration learning mode; based on the extracted eyeground features, training a detection model on a training set by using a reverse gradient propagation mode in deep learning, and finely adjusting parameters of the detection model; and finally,obtaining a detection model capable of distinguishing eight classifications including normal eyeground, high myopia, glaucoma, maculopathy, vein occlusion, diabetic retinopathy, hypertensive retinopathy and other degeneration. According to the detection method disclosed by the invention, automatic detection for multiple diseases of patients with eyeground diseases is realized, and the detection method is high in precision and high in speed.

Description

technical field [0001] The invention relates to a deep learning-based detection method for classification of multiple fundus diseases, belonging to the field of medical image processing. Background technique [0002] In recent years, the incidence of many fundus diseases has been high and the harm is serious. Fundus color photo examination is the most convenient and effective way to find fundus diseases. Doctors use professional fundus cameras to collect fundus images and then conduct manual diagnosis. Fundus examination requires professional fundus doctors and examination equipment. The investment in equipment is high, and it is difficult to train doctors. However, there are a large number of people in need in primary hospitals and physical examination centers, and the problem is very serious. Even if the number of instruments and doctors is sufficient, the diagnosis is very dependent on the personal status and experience of the doctors. Therefore, with the continuous dev...

Claims

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Application Information

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IPC IPC(8): A61B3/12A61B3/14G06T5/00G06T5/40G06T5/50G06T7/00
CPCA61B3/12A61B3/14G06T5/002G06T5/003G06T5/009G06T5/40G06T5/50G06T7/0012G06T2207/20076G06T2207/20081G06T2207/20084G06T2207/20221G06T2207/30041
Inventor 陈颍锶万程杨卫华
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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