Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination
Microscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnost...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/17/10/2167 |
| _version_ | 1818037066897817600 |
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| author | Luís Rosado José M. Correia da Costa Dirk Elias Jaime S. Cardoso |
| author_facet | Luís Rosado José M. Correia da Costa Dirk Elias Jaime S. Cardoso |
| author_sort | Luís Rosado |
| collection | DOAJ |
| description | Microscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnostic tools are increasingly required, as malaria control programs extend parasite-based diagnosis and the prevalence decreases. This paper presents an image processing and analysis methodology using supervised classification to assess the presence of malaria parasites and determine the species and life cycle stage in Giemsa-stained thin blood smears. The main differentiation factor is the usage of microscopic images exclusively acquired with low cost and accessible tools such as smartphones, a dataset of 566 images manually annotated by an experienced parasilogist being used. Eight different species-stage combinations were considered in this work, with an automatic detection performance ranging from 73.9% to 96.2% in terms of sensitivity and from 92.6% to 99.3% in terms of specificity. These promising results attest to the potential of using this approach as a valid alternative to conventional microscopy examination, with comparable detection performances and acceptable computational times. |
| first_indexed | 2024-12-10T07:20:56Z |
| format | Article |
| id | doaj.art-dd016a2baa5a4159b517334869c17591 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T07:20:56Z |
| publishDate | 2017-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-dd016a2baa5a4159b517334869c175912022-12-22T01:57:50ZengMDPI AGSensors1424-82202017-09-011710216710.3390/s17102167s17102167Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage DeterminationLuís Rosado0José M. Correia da Costa1Dirk Elias2Jaime S. Cardoso3Fraunhofer Portugal AICOS, Rua Alfredo Allen 455/461, 4200-135 Porto, PortugalInstituto Nacional de Saúde Dr. Ricardo Jorge, Rua Alexandre Herculano 321, 4000-055 Porto, PortugalFraunhofer Portugal AICOS, Rua Alfredo Allen 455/461, 4200-135 Porto, PortugalINESC TEC (Institute for Systems and Computer Engineering, Technology and Science) and Department of Electrical and Computer Engineering of the Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, PortugalMicroscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnostic tools are increasingly required, as malaria control programs extend parasite-based diagnosis and the prevalence decreases. This paper presents an image processing and analysis methodology using supervised classification to assess the presence of malaria parasites and determine the species and life cycle stage in Giemsa-stained thin blood smears. The main differentiation factor is the usage of microscopic images exclusively acquired with low cost and accessible tools such as smartphones, a dataset of 566 images manually annotated by an experienced parasilogist being used. Eight different species-stage combinations were considered in this work, with an automatic detection performance ranging from 73.9% to 96.2% in terms of sensitivity and from 92.6% to 99.3% in terms of specificity. These promising results attest to the potential of using this approach as a valid alternative to conventional microscopy examination, with comparable detection performances and acceptable computational times.https://www.mdpi.com/1424-8220/17/10/2167image analysismalariacomputer-aided diagnosismicroscopymobile devices |
| spellingShingle | Luís Rosado José M. Correia da Costa Dirk Elias Jaime S. Cardoso Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination Sensors image analysis malaria computer-aided diagnosis microscopy mobile devices |
| title | Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination |
| title_full | Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination |
| title_fullStr | Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination |
| title_full_unstemmed | Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination |
| title_short | Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination |
| title_sort | mobile based analysis of malaria infected thin blood smears automated species and life cycle stage determination |
| topic | image analysis malaria computer-aided diagnosis microscopy mobile devices |
| url | https://www.mdpi.com/1424-8220/17/10/2167 |
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