Shenzhen Sibionics Technology Co., Ltd.

Shenzhen Sibright Technology Co., Ltd.

Xiaoxin Li

Juan Wang

Yu Zhao

Bin Xia

Yujing Bai

Zhigang Hu

Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus () configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus () configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Retinopathy recognition system

Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network () configured to generate a first advanced feature set from the target fundus image, a second neural network () configured to generate a second advanced feature set from the reference fundus image, a feature combination module () configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network () configured to generate, according to the feature combination set, a judgmental result of lesions.

System for recognizing diabetic retinopathy

The present disclosure provides an artificial neural network system for identifying a lesion in a retinal fundus image that comprises a pre-processing module configured to separately pre-process a target retinal fundus image and a reference retinal fundus image taken from a same person; a first neural network () configured to generate a first advanced feature set from the target retinal fundus image; a second neural network () configured to generate a second advanced feature set from the reference retinal fundus image; a feature combination module () configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set; and a third neural network () configured to generate, according to the feature combination set, a diagnosis result. By using a target retinal fundus image and a reference retinal fundus image as independent input information, the artificial neural network may simulate a doctor, determining lesions on the target retinal fundus image using other retinal fundus images from the same person as a reference, thereby enhancing the diagnosis accuracy.

Artificial neural network and system for identifying lesion in retinal fundus image

Diabetic retinopathy recognition system based on fundus image

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Shenzhen, China

Xiaoxin Li