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

Abstract

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.