Centernet-based detection model and U-net-based multi-class segmentation model for gastrointestinal diseases

From the perspective of the computer-aided diagnosis system, it is important to build automated techniques that detect and diagnose lesions to reduce the missing rate of clinicians. Recently, various diagnosis techniques using computer vision and artificial intelligence have been developed.However, they need to diagnose various lesions more accurately to be used in actual clinical practice. Accordingly, we developed CenterNet-based object detection model and U-Net-based class-wise binary segmentation model. These models were trained with random augmentation methods including color and morphological changes. For the 43 test set images, our model shows 0.1932 ± 0.0622 of mean average precision with standard deviation in detection, and 0.2544 ± 0.2080 of semantic score in segmentation.