Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears
Malaria is a significant health concern in many third-world countries, especially for pregnant women and young children. It accounted for about 229 million cases and 600,000 mortality globally in 2019. Hence, rapid and accurate detection is vital. This study is focused on achieving three goals. The...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/12/11/2702 |
| _version_ | 1827646828336644096 |
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| author | Dilber Uzun Ozsahin Mubarak Taiwo Mustapha Basil Bartholomew Duwa Ilker Ozsahin |
| author_facet | Dilber Uzun Ozsahin Mubarak Taiwo Mustapha Basil Bartholomew Duwa Ilker Ozsahin |
| author_sort | Dilber Uzun Ozsahin |
| collection | DOAJ |
| description | Malaria is a significant health concern in many third-world countries, especially for pregnant women and young children. It accounted for about 229 million cases and 600,000 mortality globally in 2019. Hence, rapid and accurate detection is vital. This study is focused on achieving three goals. The first is to develop a deep learning framework capable of automating and accurately classifying malaria parasites using microscopic images of thin and thick peripheral blood smears. The second is to report which of the two peripheral blood smears is the most appropriate for use in accurately detecting malaria parasites in peripheral blood smears. Finally, we evaluate the performance of our proposed model with commonly used transfer learning models. We proposed a convolutional neural network capable of accurately predicting the presence of malaria parasites using microscopic images of thin and thick peripheral blood smears. Model evaluation was carried out using commonly used evaluation metrics, and the outcome proved satisfactory. The proposed model performed better when thick peripheral smears were used with accuracy, precision, and sensitivity of 96.97%, 97.00%, and 97.00%. Identifying the most appropriate peripheral blood smear is vital for improved accuracy, rapid smear preparation, and rapid diagnosis of patients, especially in regions where malaria is endemic. |
| first_indexed | 2024-03-09T19:09:52Z |
| format | Article |
| id | doaj.art-f052c9cadeb6436da6d1ab24f248eede |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T19:09:52Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-f052c9cadeb6436da6d1ab24f248eede2023-11-24T04:19:33ZengMDPI AGDiagnostics2075-44182022-11-011211270210.3390/diagnostics12112702Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood SmearsDilber Uzun Ozsahin0Mubarak Taiwo Mustapha1Basil Bartholomew Duwa2Ilker Ozsahin3Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab EmiratesOperational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, TurkeyOperational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, TurkeyOperational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, TurkeyMalaria is a significant health concern in many third-world countries, especially for pregnant women and young children. It accounted for about 229 million cases and 600,000 mortality globally in 2019. Hence, rapid and accurate detection is vital. This study is focused on achieving three goals. The first is to develop a deep learning framework capable of automating and accurately classifying malaria parasites using microscopic images of thin and thick peripheral blood smears. The second is to report which of the two peripheral blood smears is the most appropriate for use in accurately detecting malaria parasites in peripheral blood smears. Finally, we evaluate the performance of our proposed model with commonly used transfer learning models. We proposed a convolutional neural network capable of accurately predicting the presence of malaria parasites using microscopic images of thin and thick peripheral blood smears. Model evaluation was carried out using commonly used evaluation metrics, and the outcome proved satisfactory. The proposed model performed better when thick peripheral smears were used with accuracy, precision, and sensitivity of 96.97%, 97.00%, and 97.00%. Identifying the most appropriate peripheral blood smear is vital for improved accuracy, rapid smear preparation, and rapid diagnosis of patients, especially in regions where malaria is endemic.https://www.mdpi.com/2075-4418/12/11/2702blood smeardetectionmalariaparasitetransfer learning |
| spellingShingle | Dilber Uzun Ozsahin Mubarak Taiwo Mustapha Basil Bartholomew Duwa Ilker Ozsahin Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears Diagnostics blood smear detection malaria parasite transfer learning |
| title | Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears |
| title_full | Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears |
| title_fullStr | Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears |
| title_full_unstemmed | Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears |
| title_short | Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears |
| title_sort | evaluating the performance of deep learning frameworks for malaria parasite detection using microscopic images of peripheral blood smears |
| topic | blood smear detection malaria parasite transfer learning |
| url | https://www.mdpi.com/2075-4418/12/11/2702 |
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