Topological Feature Extraction from Multi-color Channels for Pattern Recognition: An Application to Fundus Image Analysis

The automated analysis of medical images is crucial for early disease detection. In recent years, deep learning has become popular for medical image analysis. In this study, we employed color-based topological features with deep learning for pattern recognition. The data topology provides information about the image's shape and global features such as connectivity and holes. We used different color channels to identify changes in topological footprints by altering the image's color. We extracted topological, local binary pattern (LBP), and Gabor features and used machine learning and deep learning models for disease classification. The model's performance was tested using three open-source fundus image databases: the Asia Pacific Tele-ophthalmology Society (APTOS 2019) data, the Optic Retinal Image Database for Glaucoma Analysis (ORIGA), and the Automatic Detection Challenge on Age-Related Macular Degeneration (ICHALLENGE-AMD). We have found that topological features from different color models provide important information for disease diagnosis.