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

Syed, Qamrun Nisa and Ismail, Amelia Ritahani (2022) Dual u-net with resnet encoder for segmentation of medical images. International Journal of Advanced Computer Science and Applications (IJACSA), 13 (12). pp. 537-542. ISSN 2158-107X E-ISSN 2156-5570

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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.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Medical Images; Deep Convolutional Neural Network; FCN; U-net; Unet_Resnet; Dual U-net with Resnet Encoder
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Amelia Ritahani Ismail
Date Deposited:	05 Jan 2023 10:21
Last Modified:	02 Jan 2024 09:42
URI:	http://irep.iium.edu.my/id/eprint/102895

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