A Hybrid System for Automatic Identification of Corneal Layers on In Vivo Confocal Microscopy Images

Accurate identification of corneal layers with in vivo confocal microscopy (IVCM) is essential for the correct assessment of corneal lesions. This project aims to obtain a reliable automated identification of corneal layers from IVCM images. A total of 7957 IVCM images were included for model training and testing. Scanning depth information and pixel information of IVCM images were used to build the classification system. Firstly, two base classifiers based on convolutional neural networks and K-nearest neighbors were constructed. Second, two hybrid strategies, namely weighted voting method and light gradient boosting machine (LightGBM) algorithm were used to fuse the results from the two base classifiers and obtain the final classification. Finally, the confidence of prediction results was stratified to help find out model errors. Both two hybrid systems outperformed the two base classifiers. The weighted area under the curve, weighted precision, weighted recall, and weighted F1 score were 0.9841, 0.9096, 0.9145, and 0.9111 for weighted voting hybrid system, and were 0.9794, 0.9039, 0.9055, and 0.9034 for the light gradient boosting machine stacking hybrid system, respectively. More than one-half of the misclassified samples were found using the confidence stratification method. The proposed hybrid approach could effectively integrate the scanning depth and pixel information of IVCM images, allowing for the accurate identification of corneal layers for grossly normal IVCM images. The confidence stratification approach was useful to find out misclassification of the system. The proposed hybrid approach lays important groundwork for the automatic identification of the corneal layer for IVCM images.