Colorectal cancer is the third most common diagnosed cancer worldwide. Early detection and removal of adenoma during the colonoscopy examination may increase the survival probability. A novel computer-aided tool for automated polyp segmentation in colonoscopy images is described in this work. SegNet, a deep convolutional neural networks has been chosen to map low resolution features with the input resolution for automated pixel-wise semantic polyp segmentation. Publicly available databases, CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB were used to train and to test the model. The outcome demonstrated the proposed method is feasible as it attains an average of 81.78, 92.35 for mean intersection over union, and dice coefficient, respectively for testing on a combination of the aforementioned datasets. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.