Classification Model of Corneal Opacity Based on Digital Image Features

Objective According to the digital image features of corneal opacity, a multi classification model of support vector machine (SVM) was established to explore the objective quantification method of corneal opacity. Methods The cornea digital images of dead pigs were collected, part of the color features and texture features were extracted according to the previous experience, and the SVM multi classification model was established. The test results of the model were evaluated by precision, sensitivity and F1 scores. The optimal feature subset was found by SVM-RFE combined with cross validation to optimize the model. Results In the classification of corneal opacity, the highest F1 score was 0.974 4, and the number of features in the optimal feature subset was 126. Conclusion The SVM multi classification model can classify the degree of corneal opacity.