Strabismus detection method based on cascade convolutional neural network

Abstract

The embodiment of the invention discloses a strabismus detection method based on a cascade convolutional neural network. The method comprises the steps that the strabismus images photographed by the camera are collated and a strabismus image library of the images is established; the cascade convolutional neural network is trained by using the face database iBUG23, LPFW24, Helen25 and AFW, and thelearning parameters in the cascade convolutional neural network are determined; the eyes of the strabismus images in the strabismus image library are segmented by using the completely trained cascadeconvolutional neural network; the eye iris of the strabismus images is segmented by using the Otsu algorithm after completing eye segmentation of the strabismus images; and whether the person has thestrabismus is determined according to the relative position relation of the iris in the eyes. The high recognition and segmentation capacity of the cascade convolutional neural network is fully utilized, whether the person has the strabismus can be efficiently and accurately determined through combination of the image processing algorithm and thus strabismus diagnosis and treatment of the patientcan be facilitated for the doctor.

Description

technical field [0001] The invention relates to the field of image processing and pattern recognition, in particular to a squint detection method based on a convolutional neural network. Background technique [0002] Strabismus is an eye disease that usually occurs in childhood. It is usually caused by problems with the eye nerves, brain or extraocular muscles. Generally, if patients with strabismus do not receive reasonable treatment, they will worsen and develop into amblyopia, and once it degenerates, it may lead to blindness. At the same time, strabismus seriously affects the appearance, and may cause withdrawn, low self-esteem and abnormal psychology in patients with squinted eyes. [0003] It can be seen from the above that squint detection is very important for the prevention and treatment of squint. At present, the detection of the subject's squint is mainly carried out manually. Generally, trained clinicians often use the "Hirschberg test (Hirschberg test)" to eva...

AI Technical Summary

Problems solved by technology

However, when each eye of the subject has a small difference, even experienced clinicians can easily ignore it, resulting in misjudgment and misdiagnosis, etc., and the efficiency of manual detection is not high

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