Comparison of U-net’s variants for segmentation of polyp images

Ismail, Amelia Ritahani and Nisa, Syed Qamrun (2023) Comparison of U-net’s variants for segmentation of polyp images. International Journal on Perceptive and Cognitive Computing (IJPCC), 9 (2). pp. 93-97. E-ISSN 2462-229X

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Abstract

Medical image analysis involves examining pictures acquired by medical imaging technologies in order to address clinical issues. The aim is to increase the quality of clinical diagnosis and extract useful information. Automatic segmentation based on deep learning (DL) techniques has gained popularity recently. In contrast to the conventional manual learning method, a neural network can now automatically learn image features. One of the most crucial convolutional neural network (CNN) semantic segmentation frameworks is U-net. It is frequently used for classification, anatomical segmentation, and lesion segmentation in the field of medical image analysis. This network framework's benefit is that it not only effectively processes and objectively evaluates medical images, properly segments the desired feature target, and helps to increase the accuracy of medical image-based diagnosis.

Item Type:	Article (Journal)
Uncontrolled Keywords:	segmentation, medical images, deep learning, Convolutional Neural Network.
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 Kulliyyah of Information and Communication Technology 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:	27 Aug 2023 02:20
Last Modified:	23 Jan 2024 17:12
URI:	http://irep.iium.edu.my/id/eprint/106269

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