Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning

Deep Learning is progressively becoming popular for computer based automated diagnosis of diseases. Deep Learning algorithms necessitate a large amount of data for training which is hard to acquire for medical problems. Similarly, annotation of medical images can be done with the help of specialized...

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Main Authors:	Amin, I., Hassan, S., Jaafar, J.
Format:	Conference or Workshop Item
Published:	Institute of Electrical and Electronics Engineers Inc. 2020
Online Access:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097534590&doi=10.1109%2fICCI51257.2020.9247724&partnerID=40&md5=96dfedfc3c60cf2c4a670bc679a73c78 http://eprints.utp.edu.my/29860/
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Institution:	Universiti Teknologi Petronas

id	my.utp.eprints.29860
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spelling	my.utp.eprints.298602022-03-25T02:58:21Z Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning Amin, I. Hassan, S. Jaafar, J. Deep Learning is progressively becoming popular for computer based automated diagnosis of diseases. Deep Learning algorithms necessitate a large amount of data for training which is hard to acquire for medical problems. Similarly, annotation of medical images can be done with the help of specialized doctors only. This paper presents a semi-supervised learning based model that combines the capabilities of generative adversarial network (GAN) and transfer learning. The proposed model does not demand a large amount of data and can be trained using a small number of images. To evaluate the performance of the model, it is trained and tested on publicly available chest Xray dataset. Better classification accuracy of 94.73 is achieved for normal X-ray images and the ones with pneumonia. Â© 2020 IEEE. Institute of Electrical and Electronics Engineers Inc. 2020 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097534590&doi=10.1109%2fICCI51257.2020.9247724&partnerID=40&md5=96dfedfc3c60cf2c4a670bc679a73c78 Amin, I. and Hassan, S. and Jaafar, J. (2020) Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning. In: UNSPECIFIED. http://eprints.utp.edu.my/29860/
institution	Universiti Teknologi Petronas
building	UTP Resource Centre
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Petronas
content_source	UTP Institutional Repository
url_provider	http://eprints.utp.edu.my/
description	Deep Learning is progressively becoming popular for computer based automated diagnosis of diseases. Deep Learning algorithms necessitate a large amount of data for training which is hard to acquire for medical problems. Similarly, annotation of medical images can be done with the help of specialized doctors only. This paper presents a semi-supervised learning based model that combines the capabilities of generative adversarial network (GAN) and transfer learning. The proposed model does not demand a large amount of data and can be trained using a small number of images. To evaluate the performance of the model, it is trained and tested on publicly available chest Xray dataset. Better classification accuracy of 94.73 is achieved for normal X-ray images and the ones with pneumonia. Â© 2020 IEEE.
format	Conference or Workshop Item
author	Amin, I. Hassan, S. Jaafar, J.
spellingShingle	Amin, I. Hassan, S. Jaafar, J. Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning
author_facet	Amin, I. Hassan, S. Jaafar, J.
author_sort	Amin, I.
title	Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning
title_short	Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning
title_full	Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning
title_fullStr	Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning
title_full_unstemmed	Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning
title_sort	semi-supervised learning for limited medical data using generative adversarial network and transfer learning
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2020
url	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097534590&doi=10.1109%2fICCI51257.2020.9247724&partnerID=40&md5=96dfedfc3c60cf2c4a670bc679a73c78 http://eprints.utp.edu.my/29860/
_version_	1738657025704853504

Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning

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