An Automatic and Cost-Effective Parasitemia Identification Framework for Low-End Microscopy Imaging Devices

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dc.contributor.authorMaiseli, Baraka J.
dc.contributor.authorMei, Jiangyuan
dc.contributor.authorGao, Huijun
dc.contributor.authorYin, Shen
dc.date.accessioned2016-09-09T10:06:58Z
dc.date.available2016-09-09T10:06:58Z
dc.date.issued2014
dc.descriptionFull text can be accessed at http://ieeexplore.ieee.org/document/7231926/en_US
dc.description.abstractIn the detection of Malarial parasites from a patient, it is usually necessary to carefully examine the corresponding blood-slide smear and distinguish the infected and healthy Red Blood Cells (RBCs). If this process is done manually, as evidenced in common traditional approaches, the following challenges may be encountered: inaccuracy of the lab results, which originates from normal human errors or lack of experience of a person conducting diagnosis, and large processing times. Consequently, doctors and specialists are likely to provide improper prescriptions to patients. With the improvement of the computational power of computers, however, the whole diagnosis process can be automated. Several methods in literature have been proposed for this purpose. Most of these methods demand the availability of high-end microscopy imaging systems to generate reliable and accurate results. Such costly advanced devices may not be afforded by developing countries with sluggish economic growth. In this paper, therefore, we have developed a cost-effective framework which can address the mentioned challenge. Our approach introduces a Super Resolution (SR) model into the existing framework to enhance the resolution of the input images before letting them subjected to the subsequent detection stages. This provides a possibility for applying the low-end microscopy devices capable of capturing Low Resolution (LR) blood smear images for identifying the degree of Malaria in a patient. In the proposed framework, the SR component uses the nonlinear Charbonnier diffusion model in the regularization part because of its good regularity characteristics. Experimental results demonstrate strong correlation of our method and the manual one.en_US
dc.identifier.citationMaiseli, B., Mei, J., Gao, H. and Yin, S., 2014, July. An automatic and cost-effective parasitemia identification framework for low-end microscopy imaging devices. In Mechatronics and Control (ICMC), 2014 International Conference on (pp. 2048-2053). IEEE.en_US
dc.identifier.doi10.1109/ICMC.2014.7231926
dc.identifier.urihttp://hdl.handle.net/20.500.11810/3692
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectDiseasesen_US
dc.subjectImage resolutionen_US
dc.subjectBlooden_US
dc.subjectManualsen_US
dc.subjectMathematical modelen_US
dc.subjectMicroscopyen_US
dc.titleAn Automatic and Cost-Effective Parasitemia Identification Framework for Low-End Microscopy Imaging Devicesen_US
dc.typeJournal Articleen_US
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