Diabetic retina eye ground image pathology detection method

Abstract

The invention discloses a diabetic retina eye ground image pathology detection method comprising the following steps: preprocessing a medical eye ground image so as to form a standard eye ground image; dividing areas for the eye ground image so as to form m sub-images of different eye ground areas; using a multilayer area convolution nerve network model to extract an area depth feature vector of the eye ground sub-image; using the area depth feature vector as the LSTM nerve network input, predicting correlations of different areas, and forming an eye ground image global feature vector; finally, using a whole articulamentum and softmax to realize multi-classification detection of the eye ground image. The method is based on retina eye ground images and labels that can be crawled on the internet, uses the correlation between the eye ground image area depth features and the adjacent area, and realizes the diabetic retina eye ground image pathology automatic detection via the convolution nerve network and recursion nerve network algorithms, thus effectively improving the detection accuracy and timeliness.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and more particularly relates to a method for detecting diabetic retinal fundus image lesions Background technique [0002] Diabetic retinopathy is the most important causative eye disease in the US and European populations. According to the World Health Organization, by 2030, the number of patients with retinopathy worldwide will increase to 366 million, and diabetes prevention and treatment will become a more serious worldwide problem. [0003] Studies have shown that early diagnosis and treatment of diabetic retinopathy patients can effectively prevent vision loss and blindness, and the key to prevention and treatment is fundus photography, regular follow-up to detect the progress of the disease, and timely laser intervention. However, at present, more than 50% of the patients in the world have not received any form of eye examination, and the examination of diabetic retinopathy bas...

AI Technical Summary

Problems solved by technology

However, more than 50% of patients in the world have not received any form of eye examination, and the examination of diabetic retinopathy based on fundus images is basically carried out by ophthalmologists' naked eyes.

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