Polyp Segmentation with Deep Learning: Utilizing DeeplabV3+ Architecture and Various CNN Backbones

Polyps are abnormal tissue growths that often serve as early indicators for various types of cancer. Early detection is crucial in the treatment of diseases like colorectal cancer, which has a high mortality rate. There is a significant need for automated diagnostic systems to detect these cancers efficiently. This article introduces a deep learning-based model utilizing the Deeplabv3+ architecture, which has been augmented with four different convolutional neural network backbones. The enhanced architectures have been tested on the publicly available Kvasir-SEG and CVC-ClinicDB datasets for the task of polyp segmentation. Experimental studies have shown that the best results for the Kvasir-SEG dataset were achieved using the ResNet50 architecture, while the highest performance on the CVC-ClinicDB dataset was obtained with the SqueezeNet architecture.