부산대학교 도서관

In this paper, a method of achieving high accuracy in discriminating right and left eyes is proposed for ophthalmic surgery. A VGG16 convolutional neural network is employed to construct a main classifier. The data presented to the main classifier are some frames sampled at regular intervals from surgery videos. Before classifying the frames to be examined, the proposed method determines whether they are suitable or not to improve the discrimination accuracy as high as possible. In other words, the frames causing erroneous discrimination are omitted. The determination of frames depends on image characteristics associated with lightness values and edges, and on positions of eyelid speculums in them. It is based on SegNet neural networks. The proposed method determines the frames to be presented, if rectangular areas specified by bent parts of the speculums adequately appear in the predefined regions inside them. Experimental results reveal that the proposed method achieves the favorable discrimination accuracy with a small number of data in training SegNet networks compared with another method.