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 Moment Estimation (ADAM) and Root Mean Squared Propagation (RMSPROP) optimizers received the highest score of 43 in the ranking, DiffGrad and Nesterov-accelerated Adaptive Moment Estimation (NADAM) ranked second with the score of 13, the Adaptive Delta (ADADELTA) ranked third with a score of 2, whereas Stochastic Gradient Descent (SGD), Adaptive Gradient Descent (ADAGRAD), and Adaptive Max (ADAMAX) optimizers performed least in the evaluation. Based on the deep learning application, the optimizer employed varies by considering computational speed, memory and computational time. This preliminary research provides the necessary key information for consideration in the development of an architecture with utilization of an optimizer.
