Abstract: 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 (12) configured to generate a first advanced feature set from the target fundus image, a second neural network (22) configured to generate a second advanced feature set from the reference fundus image, a feature combination module (13) 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 (14) configured to generate, according to the feature combination set, a judgmental result of lesions.

Abstract: Some embodiments of the disclosure describe an evaluation system based on analyte data including a memory storing a program, a display, and a processor. In some examples, the processor is configured to: receive analyte data during a predetermined time period from an object to be tested, acquire a plurality of analyte indicators different from each other based on the analyte data, normalize the plurality of analyte indicators to a predetermined range to acquire a plurality of normalized analyte indicators, plot a polygon pattern corresponding to the analyte data during the first time period as a target polygon pattern, plot a polygon pattern corresponding to the analyte data during the second time period as a reference polygon pattern, and take a line segment between a vertex and a center point as an axis, and display the target polygon pattern and the reference polygon pattern.

Abstract: 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 (12) configured to generate a first advanced feature set from the target fundus image, a second neural network (22) configured to generate a second advanced feature set from the reference fundus image, a feature combination module (13) 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 (14) configured to generate, according to the feature combination set, a judgmental result of lesions.

Abstract: 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 (1) 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 (2) 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.

Abstract: A pulse current generation circuit (100) for neural stimulation includes an analogue signal receiving device (101) for receiving an analogue signal; an analogue-to-digital converter (102) for converting the analogue signal into a digital control signal; a current signal controller (103) for producing, according to the digital control signal, pulse current parameters for generating bidirectional pulse current signals; and a current generator (104) for generating, according to the pulse current parameters, bidirectional pulse current signals for neural stimulation, and the current generator can generate pulse currents of different precisions according to the pulse current parameters. In addition, the present invention further relates to a charge compensation circuit, a charge compensation method, and an implantable electrical retina stimulator using the pulse current generation circuit or the charge compensation circuit.

Abstract: A pulse current generation circuit (100) for neural stimulation includes an analogue signal receiving device (101) for receiving an analogue signal; an analogue-to-digital converter (102) for converting the analogue signal into a digital control signal; a current signal controller (103) for producing, according to the digital control signal, pulse current parameters for generating bidirectional pulse current signals; and a current generator (104) for generating, according to the pulse current parameters, bidirectional pulse current signals for neural stimulation, and the current generator can generate pulse currents of different precisions according to the pulse current parameters. In addition, the present invention further relates to a charge compensation circuit, a charge compensation method, and an implantable electrical retina stimulator using the pulse current generation circuit or the charge compensation circuit.

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 (12) configured to generate a first advanced feature set from the target retinal fundus image; a second neural network (22) configured to generate a second advanced feature set from the reference retinal fundus image; a feature combination module (13) 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 (14) configured to generate, according to the feature combination set, a diagnosis result.

Abstract: 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 (1) 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 (2) 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.

Abstract: A pulse current generation circuit (100) for neural stimulation includes an analogue signal receiving device (101) for receiving an analogue signal; an analogue-to-digital converter (102) for converting the analogue signal into a digital control signal; a current signal controller (103) for producing, according to the digital control signal, pulse current parameters for generating bidirectional pulse current signals; and a current generator (104) for generating, according to the pulse current parameters, bidirectional pulse current signals for neural stimulation, and the current generator can generate pulse currents of different precisions according to the pulse current parameters. In addition, the present invention further relates to a charge compensation circuit, a charge compensation method, and an implantable electrical retina stimulator using the pulse current generation circuit or the charge compensation circuit.