Abstract: A pattern recognition method of the immunofluorescence images of autoantibody identification is disclosed. The method includes the following steps: inputting a plurality of original cell immunofluorescence images; conducting an operation of a plurality of convolutional neural networks by a processor, the plurality of convolutional neural networks include a convolution layer, a pooling layer and an inception layer for capturing the plurality of convolution features; conducting a judgment process to obtain the proportions of the antinuclear antibodies morphological patterns; and outputting the recognition results.

Abstract: A classification system and a classification method of the autoantibody immunofluorescence image are disclosed. The system includes an input device, a processor and an output device. The plurality of cell immunofluorescence images and the corresponded extractable nuclear antigen results are input through the input device. The processor conducts a plurality of convolution neural network calculation and output the classification based on the extractable nuclear antigen results, so as to obtain an extractable nuclear antigen classification model. When the autoantibody immunofluorescence image is input by the input device, the corresponded classification of the extractable nuclear antigen can be predicted. The classification result is output through the output device.

Abstract: A classification system and a classification method of the autoantibody immunofluorescence image are disclosed. The system includes an input device, a processor and an output device. The plurality of cell immunofluorescence images and the corresponded extractable nuclear antigen results are input through the input device. The processor conducts a plurality of convolution neural network calculation and output the classification based on the extractable nuclear antigen results, so as to obtain an extractable nuclear antigen classification model. When the autoantibody immunofluorescence image is input by the input device, the corresponded classification of the extractable nuclear antigen can be predicted. The classification result is output through the output device.

Abstract: A pattern recognition method of the immunofluorescence images of autoantibody identification is disclosed. The method includes the following steps: inputting a plurality of original cell immunofluorescence images; conducting an operation of a plurality of convolutional neural networks by a processor, the plurality of convolutional neural networks include a convolution layer, a pooling layer and an inception layer for capturing the plurality of convolution features; conducting a judgment process to obtain the proportions of the antinuclear antibodies morphological patterns; and outputting the recognition results.