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

Authors : Yaren Akgöl, Buket Toptaş

Pages : 797-805

Doi:10.24012/dumf.1517112

View : 249 | Download : 275

Publication Date : 2024-12-23

Article Type : Research Paper

Abstract :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.
Keywords : Polip Bölütleme, Deeplabv3+, kolorektal kanser, kolon kanseri