On Automatic Detection of Central Serous Chorioretinopathy and Central Exudative Chorioretinopathy in Fundus Images

Automatic detection of chorioretinopathy plays an important role in clinical practice, but the detection of a major chorioretinopathy of central serous chorioretinopathy based on fundus photography images has rarely been studied, let alone distinguishing it from another chorioretinopathy of central exudative chorioretinopathy. Due to the high degree of similarity between the two chorioretinopathies on fundus images, it is difficult for the latest automatic methods to accurately distinguish between them. In this study, we design a deep neural network with two branches for different classification tasks, where the first one is to distinguish the normal and abnormal while the other is to classify the two chorioretinopathies. We manage to improve the classification accuracy by combining focal loss and discriminative loss. Extensive experiments are conducted for comparison between our method and other universal classification models using a private retinal fundus dataset. The results demonstrate that our method achieves the best performance with 97.69%, 99.58% and 98.87% on the accuracy, precision and sensitivity, respectively.