Establishment of an automatic diagnosis system for corneal endothelium diseases using artificial intelligence

To use artificial intelligence to establish an automatic diagnosis system for corneal endothelium diseases (CEDs). We develop an automatic system for detecting multiple common CEDs involving an enhanced compact convolutional transformer (ECCT). Specifically, we introduce a cross-head relative position encoding scheme into a standard self-attention module to capture contextual information among different regions and employ a token-attention feed-forward network to place greater focus on valuable abnormal regions. A total of 2723 images from CED patients are used to train our system. It achieves an accuracy of 89.53