Performance evaluation of medical image denoising using convolutional autoencoders

Convolutional Autoencoders (CAEs) are neural net- work architectures specifically designed for image-processing tasks. CAEs also show a great performance in image denoising as it has the capability to efficiently eliminate noise from images while retaining crucial features and structural integrity....

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Main Authors: Ismail, Amelia Ritahani, Azhary, Muhammad Zulhazmi Rafiqi, Noor Azwan, Nor Aiman Zaharin, Ismail, Ahsiah, Alsaiari, Nisrin Amer
Format: Proceeding Paper
Language:English
Published: 2024
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Online Access:http://irep.iium.edu.my/116733/1/Performance_Evaluation_of_Medical_Image_Denoising_using_Convolutional_Autoencoders%203.pdf
http://irep.iium.edu.my/116733/
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spelling my.iium.irep.1167332024-12-18T02:22:36Z http://irep.iium.edu.my/116733/ Performance evaluation of medical image denoising using convolutional autoencoders Ismail, Amelia Ritahani Azhary, Muhammad Zulhazmi Rafiqi Noor Azwan, Nor Aiman Zaharin Ismail, Ahsiah Alsaiari, Nisrin Amer QA75 Electronic computers. Computer science Convolutional Autoencoders (CAEs) are neural net- work architectures specifically designed for image-processing tasks. CAEs also show a great performance in image denoising as it has the capability to efficiently eliminate noise from images while retaining crucial features and structural integrity. However, the effectiveness of CAEs in denoising tasks can be limited by the specific characteristics and complexities present in the images; for example, different intensities and distribution of noise will lead to challenges in achieving consistent and high- quality denoising results across diverse datasets like medical images. This paper, therefore, explores the capabilities of CAEs in effectively denoising images corrupted by varying levels of Gaussian noise. The CAE’s performance is evaluated under different levels of noise severity using two datasets: PathMNIST and Brain MRI Scan images. Findings indicate that at a low noise level, CAEs achieve impressive results with PathMNIST images, demonstrating high image fidelity. However, Brain MRI Scan images show moderate denoising suggesting opportunities for improvement. As noise severity increases, both datasets experience reduced denoising performance, highlighting the need for enhanced CAE configurations to maintain image quality in critical applications like medical diagnosis. 2024-10-14 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/116733/1/Performance_Evaluation_of_Medical_Image_Denoising_using_Convolutional_Autoencoders%203.pdf Ismail, Amelia Ritahani and Azhary, Muhammad Zulhazmi Rafiqi and Noor Azwan, Nor Aiman Zaharin and Ismail, Ahsiah and Alsaiari, Nisrin Amer (2024) Performance evaluation of medical image denoising using convolutional autoencoders. In: 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT), 7 Agust 2024, IIUM, Kuala Lumpur.
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ismail, Amelia Ritahani
Azhary, Muhammad Zulhazmi Rafiqi
Noor Azwan, Nor Aiman Zaharin
Ismail, Ahsiah
Alsaiari, Nisrin Amer
Performance evaluation of medical image denoising using convolutional autoencoders
description Convolutional Autoencoders (CAEs) are neural net- work architectures specifically designed for image-processing tasks. CAEs also show a great performance in image denoising as it has the capability to efficiently eliminate noise from images while retaining crucial features and structural integrity. However, the effectiveness of CAEs in denoising tasks can be limited by the specific characteristics and complexities present in the images; for example, different intensities and distribution of noise will lead to challenges in achieving consistent and high- quality denoising results across diverse datasets like medical images. This paper, therefore, explores the capabilities of CAEs in effectively denoising images corrupted by varying levels of Gaussian noise. The CAE’s performance is evaluated under different levels of noise severity using two datasets: PathMNIST and Brain MRI Scan images. Findings indicate that at a low noise level, CAEs achieve impressive results with PathMNIST images, demonstrating high image fidelity. However, Brain MRI Scan images show moderate denoising suggesting opportunities for improvement. As noise severity increases, both datasets experience reduced denoising performance, highlighting the need for enhanced CAE configurations to maintain image quality in critical applications like medical diagnosis.
format Proceeding Paper
author Ismail, Amelia Ritahani
Azhary, Muhammad Zulhazmi Rafiqi
Noor Azwan, Nor Aiman Zaharin
Ismail, Ahsiah
Alsaiari, Nisrin Amer
author_facet Ismail, Amelia Ritahani
Azhary, Muhammad Zulhazmi Rafiqi
Noor Azwan, Nor Aiman Zaharin
Ismail, Ahsiah
Alsaiari, Nisrin Amer
author_sort Ismail, Amelia Ritahani
title Performance evaluation of medical image denoising using convolutional autoencoders
title_short Performance evaluation of medical image denoising using convolutional autoencoders
title_full Performance evaluation of medical image denoising using convolutional autoencoders
title_fullStr Performance evaluation of medical image denoising using convolutional autoencoders
title_full_unstemmed Performance evaluation of medical image denoising using convolutional autoencoders
title_sort performance evaluation of medical image denoising using convolutional autoencoders
publishDate 2024
url http://irep.iium.edu.my/116733/1/Performance_Evaluation_of_Medical_Image_Denoising_using_Convolutional_Autoencoders%203.pdf
http://irep.iium.edu.my/116733/
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score 13.222552