Abstract:

The current researches on the computer-aided diagnosis of an ophthalmic disease focus mainly on the automatic classification or grading based on the currently available images, the method for the prediction of an ophthalmic disease is scarce, and therefore, a cost-sensitive temporal sequence method is proposed to analyze and predict the development trend of an ophthalmic disease. First, the Canny edge detector operator and Hough transform are used to preprocess the slit-lamp image and obtain the lens area. Second, the residual convolutional neural network is employed to extract the high-level features from the lens area, which are then inputted into the long short term memory network to mine the inherent laws between the temporal sequence data. Finally, the cost-sensitive Softmax classifier is used to predict the development trend of an ophthalmic disease. Experimental results prove that this method has higher accuracy and sensitivity for prediction, and can simultaneously predict different sequence data with a length of 3～5.

Key words: convolutional neural network, long short term memory, cost-sensitive, sequence images, ophthalmic disease prediction