Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification
<p dir="ltr">Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are time-consuming that require a great deal of human expertise and efforts. Computer-based automated diagnosis of diseases is progressively becoming...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2023
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513507690545152 |
|---|---|
| author | Ibrar Amin (19438018) |
| author2 | Saima Hassan (14918003) Samir Brahim Belhaouari (16855434) Muhammad Hamza Azam (19438021) |
| author2_role | author author author |
| author_facet | Ibrar Amin (19438018) Saima Hassan (14918003) Samir Brahim Belhaouari (16855434) Muhammad Hamza Azam (19438021) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ibrar Amin (19438018) Saima Hassan (14918003) Samir Brahim Belhaouari (16855434) Muhammad Hamza Azam (19438021) |
| dc.date.none.fl_str_mv | 2023-12-28T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.32604/cmc.2023.033860 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Transfer_Learning-Based_Semi-Supervised_Generative_Adversarial_Network_for_Malaria_Classification/26772184 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Public health Information and computing sciences Data management and data science Generative adversarial network transfer learning semi-supervised malaria VGG16 |
| dc.title.none.fl_str_mv | Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are time-consuming that require a great deal of human expertise and efforts. Computer-based 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 malaria-infected and normal class) and achieved a classification accuracy of 96.6%.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers, Materials & Continua<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.32604/cmc.2023.033860" target="_blank">https://dx.doi.org/10.32604/cmc.2023.033860</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8de64985504d5de51d2ee87b94acfaed |
| identifier_str_mv | 10.32604/cmc.2023.033860 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26772184 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria ClassificationIbrar Amin (19438018)Saima Hassan (14918003)Samir Brahim Belhaouari (16855434)Muhammad Hamza Azam (19438021)Health sciencesHealth services and systemsPublic healthInformation and computing sciencesData management and data scienceGenerative adversarial networktransfer learningsemi-supervisedmalariaVGG16<p dir="ltr">Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are time-consuming that require a great deal of human expertise and efforts. Computer-based 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 malaria-infected and normal class) and achieved a classification accuracy of 96.6%.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers, Materials & Continua<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.32604/cmc.2023.033860" target="_blank">https://dx.doi.org/10.32604/cmc.2023.033860</a></p>2023-12-28T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.32604/cmc.2023.033860https://figshare.com/articles/journal_contribution/Transfer_Learning-Based_Semi-Supervised_Generative_Adversarial_Network_for_Malaria_Classification/26772184CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/267721842023-12-28T15:00:00Z |
| spellingShingle | Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification Ibrar Amin (19438018) Health sciences Health services and systems Public health Information and computing sciences Data management and data science Generative adversarial network transfer learning semi-supervised malaria VGG16 |
| status_str | publishedVersion |
| title | 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_short | Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification |
| title_sort | Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification |
| topic | Health sciences Health services and systems Public health Information and computing sciences Data management and data science Generative adversarial network transfer learning semi-supervised malaria VGG16 |