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Colorectal cancer (CRC), ranking third most prevalent cancer type, can be diagnosed with the detection of polyps in the colon and rectum through endoscopic procedures facilitating prompt treatment. During visualization of gastrointestinal tract by the physician, there is high probability of miss rates and reviewing of the images is laborious. Automatic segmentation and detection are enabled with the convolutional neural networks (CNN). We segmented the polyps from the wireless capsule endoscopy images of Kvasir dataset using various CNN models. We have presented nine optimizers for each architecture and evaluated the performance parameters. The optimizers were graded based on the performance metrics in order to provide an insight for the researchers on the selection of optimizer and architecture. On comparison of the performance metrics of the pretrained and U-net-based architecture, the Adaptive …