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Endoscopy is a widely used clinical procedure for the early detection of numerous diseases. However, the images produced are usually heavily corrupted with multiple artifacts that reduce the visualization of the underlying tissue. Moreover, the localization of actual diseased regions is also a complex problem. For that reason, EndoCV2020 challenges aim to make progress in the state-of-the-art in the detection and segmentation of artifacts and diseases in endoscopy images. In this work, we propose approaches based on U-Net and U-Net++ architecture to automate the segmentation task of EndoCV2020. We use the EfficientNet as our encoder to extract powerful features for our decoders. Data augmentation and pre-trained weights are employed to prevent overfilling and improve generalization. Test-time augmentation also helps in improving the results of our models. Our methods performs well in this challenge and achieves a score of 60.20% for the EAD2020 semantic segmentation task and 59.81% for the EDD2020’s.