Dual U-Net with Resnet Encoder for Segmentation of Medical Images

Segmentation of medical images has been the most demanding and growing area currently for analysis images. polyp is a huge challenge because variability color depth morphology in polyps throughout colonoscopy imaging. For segmentation, this work, we have used dataset gastrointestinal polyp. The algorithms paper segmentation depend on profound deep convolutional neural network architectures: FCN, Dual U-net with Resnet Encoder, U-net, Unet_Resnet. To improve performance, data augmentation performed dataset. efficiency measured by using metrics such as Dice Similarity Coefficient (DSC) Intersection Over Union (IOU). algorithm Encoder obtains higher DSC 0.87 IOU 0.80 beats other Unet_Resnet