Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification
Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts. Computerbased automated diagnosis of diseases is progressively becoming popular. Although deep learnin...
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oai:scholars.utp.edu.my:342942023-01-17T13:35:51Z http://scholars.utp.edu.my/id/eprint/34294/ Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification Amin, I. Hassan, S. Belhaouari, S.B. Azam, M.H. Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts. Computerbased automated diagnosis of diseases is progressively becoming popular. Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems. Similarly, labeling of medical images can be done with the help of medical experts only. Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system, which showed promising results. However, the most common problem with these models is that they need a large amount of data for training. This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning. The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models. Performance of the proposed model is evaluated on a publicly available dataset of blood smear images (with malariainfected and normal class) and achieved a classification accuracy of 96.6. © 2023 Tech Science Press. All rights reserved. 2023 Article NonPeerReviewed Amin, I. and Hassan, S. and Belhaouari, S.B. and Azam, M.H. (2023) Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification. Computers, Materials and Continua, 74 (3). pp. 6335-6349. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145346928&doi=10.32604%2fcmc.2023.033860&partnerID=40&md5=5af709176d85f55aca1e201e3a3d4382 10.32604/cmc.2023.033860 10.32604/cmc.2023.033860 10.32604/cmc.2023.033860 |
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Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts. Computerbased automated diagnosis of diseases is progressively becoming popular. Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems. Similarly, labeling of medical images can be done with the help of medical experts only. Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system, which showed promising results. However, the most common problem with these models is that they need a large amount of data for training. This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning. The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models. Performance of the proposed model is evaluated on a publicly available dataset of blood smear images (with malariainfected and normal class) and achieved a classification accuracy of 96.6. © 2023 Tech Science Press. All rights reserved. |
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Article |
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Amin, I. Hassan, S. Belhaouari, S.B. Azam, M.H. |
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Amin, I. Hassan, S. Belhaouari, S.B. Azam, M.H. Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification |
author_facet |
Amin, I. Hassan, S. Belhaouari, S.B. Azam, M.H. |
author_sort |
Amin, I. |
title |
Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification |
title_short |
Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification |
title_full |
Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification |
title_fullStr |
Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification |
title_full_unstemmed |
Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification |
title_sort |
transfer learning-based semi-supervised generative adversarial network for malaria classification |
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2023 |
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http://scholars.utp.edu.my/id/eprint/34294/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145346928&doi=10.32604%2fcmc.2023.033860&partnerID=40&md5=5af709176d85f55aca1e201e3a3d4382 |
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