Comparative performance analysis of Deep Convolutional Neural Network for Gastrointestinal Polyp Image Segmentation - Nisa, Syed Qamrun; Ismail, Amelia Ritahani (2021)
Image segmentation is the most challenging and emerging field nowadays for medical image analysis. Polyp image segmentation is a difficult task due to the variations in the appearance and color intensity of the polyps in colonoscopy images. In this paper, we use a dataset of gastrointestinal polyp images for segmentation. The segmentation methods for gastrointestinal polyp images in this paper are based on three deep convolutional neural network models that are FCN, U-NET, and, hybrid Unet_Resnet. Data augmentation is applied to the dataset to increase the accuracy rate. The performance of the three models is evaluated by metrics that are Intersection of Union (IOU) and Dice Similarity Coefficient (DSC). The hybrid model, Unet_Resnet achieves higher IOU, and DSC of 0.75 and 0.86 respectively, which outperforms the other two models FCN and U-Net in gastrointestinal polyp image segmentation