Dual u-net with resnet encoder for segmentation of medical images

Mohammad Fasha; Bassam Hammo; Nadim Obeid; Jabir Widian

Abstract

Segmentation of medical images has been the most demanding and growing area currently for analysis of medical images. Segmentation of polyp images is a huge challenge because of the variability of color depth and morphology in polyps throughout colonoscopy imaging. For segmentation, in this work, we have used a dataset of images of the gastrointestinal polyp. The algorithms used in this paper for segmentation of gastrointestinal polyp images depend on profound deep convolutional neural network architectures: FCN, Dual U-net with Resnet Encoder, U-net, and Unet_Resnet. To improve the performance, data augmentation is performed on the dataset. The efficiency of the algorithms is measured by using metrics such as Dice Similarity Coefficient (DSC) and Intersection Over Union (IOU). The algorithm Dual U-net with Resnet Encoder obtains a higher DSC of 0.87 and IOU of 0.80 and beats the other algorithms U-net, FCN, and Unet_Resnet in segmentation of gastrointestinal polyp images.

Keywords

Computer sciencePerceptionState (computer science)Value (mathematics)HumanityReplicateExistentialismMechanism (biology)Artificial intelligenceHuman–computer interactionMachine learning

Affiliated Institutions

Related Publications

Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Stanislav Nikolov , Sam Blackwell , Alexei Zverovitch +26 more

Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires ma...

2018 arXiv (Cornell University) 247 citations

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Nahian Siddique , Sidike Paheding , Colin Elkin +1 more

U-net is an image segmentation technique developed primarily for medical\nimage analysis that can precisely segment images using a scarce amount of\ntraining data. These traits ...

2021 IEEE Access 1687 citations

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

Zongwei Zhou , Md Mahfuzur Rahman Siddiquee , Nima Tajbakhsh +1 more

The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations...

2019 IEEE Transactions on Medical Imaging 3567 citations

Road Extraction by Deep Residual U-Net

Zhengxin Zhang , Qingjie Liu , Yunhong Wang

Road extraction from aerial images has been a hot research topic in the field\nof remote sensing image analysis. In this letter, a semantic segmentation\nneural network which co...

2018 IEEE Geoscience and Remote Sensing Le... 2795 citations

MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation

M. Sohel Rahman , Nabil Ibtehaz

In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation. In this regard, U-Net has been the most popular architecture in the medical imaging...

2019 Neural Networks 2053 citations

Publication Info

Year: 2022
Type: paratext
Citations: 1148
Access: Closed

External Links

Citation Metrics

1148

OpenAlex

Cite This

APA Style

                            
                                    Mohammad Fasha, 
                                
                                    Bassam Hammo, 
                                
                                    Nadim Obeid
                                
                                et al.
                            
                            (2022). 
                            Dual u-net with resnet encoder for segmentation of medical images. 
                            Scientific Repository (Petra Christian University)
                            
                            .