Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images
Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/5/2284 |
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| author | Asma Maqsood Muhammad Shahid Farid Muhammad Hassan Khan Marcin Grzegorzek |
| author_facet | Asma Maqsood Muhammad Shahid Farid Muhammad Hassan Khan Marcin Grzegorzek |
| author_sort | Asma Maqsood |
| collection | DOAJ |
| description | Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>96.82</mn></mrow></semantics></math></inline-formula>% accurate in detecting malaria from the microscopic blood smears. |
| first_indexed | 2024-03-09T05:31:00Z |
| format | Article |
| id | doaj.art-2444b70fa27c4b4481b0eea2bde227fa |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:31:00Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2444b70fa27c4b4481b0eea2bde227fa2023-12-03T12:32:44ZengMDPI AGApplied Sciences2076-34172021-03-01115228410.3390/app11052284Deep Malaria Parasite Detection in Thin Blood Smear Microscopic ImagesAsma Maqsood0Muhammad Shahid Farid1Muhammad Hassan Khan2Marcin Grzegorzek3Punjab University College of Information Technology, University of the Punjab, Lahore 54000, PakistanPunjab University College of Information Technology, University of the Punjab, Lahore 54000, PakistanPunjab University College of Information Technology, University of the Punjab, Lahore 54000, PakistanInstitute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, GermanyMalaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>96.82</mn></mrow></semantics></math></inline-formula>% accurate in detecting malaria from the microscopic blood smears.https://www.mdpi.com/2076-3417/11/5/2284malaria detection<i>Plasmodium</i> parasitetransfer learningconvolutional neural networkscomputer aided design (CAD) |
| spellingShingle | Asma Maqsood Muhammad Shahid Farid Muhammad Hassan Khan Marcin Grzegorzek Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images Applied Sciences malaria detection <i>Plasmodium</i> parasite transfer learning convolutional neural networks computer aided design (CAD) |
| title | Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images |
| title_full | Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images |
| title_fullStr | Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images |
| title_full_unstemmed | Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images |
| title_short | Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images |
| title_sort | deep malaria parasite detection in thin blood smear microscopic images |
| topic | malaria detection <i>Plasmodium</i> parasite transfer learning convolutional neural networks computer aided design (CAD) |
| url | https://www.mdpi.com/2076-3417/11/5/2284 |
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