Abstract: In order to let the undergraduates understand biomedical image processing technology and master deep learning methods, combined with the innovation and entrepreneurship training program of Jilin University college students, the experimental project “Research on automated analysis of corneal images based on deep learning" is completed. To assist the medical research and development of effective drugs for the treatment of CNV(Corneal Neovascularization) diseases, it is necessary to observe and obtain data on the growth of mouse corneal blood vessels under the influence of drugs. Therefore, an automated corneal image analysis program based on deep learning is designed, where the gel-processed mouse corneal image provided by the cooperative hospital is used as the research object, and the corneal features are completed through MATLAB tools and deep learning algorithms such as neural networks. SegNet semantic segmentation network and SVM(Support Vector Machine)- based image segmentation are used to achieve automatic extraction of mouse corneal images. The accuracy and reliability of corneal extraction under the two methods are analyzed. The results show that the use of SegNet semantic segmentation network is high in accuracy, and its accuracy rate can reach 97. 75% .

Key words: information science and systems science, image segmentation, deep learning, corneal