Title | Parasite Detection Model for Neglected Tropical Disease Diagnosis |
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Authors | Makram Mobarak Sinada, F., Reni, S. and Kale, I. |
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Type | Conference poster |
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Abstract | Kato-katz is the most commonly used microscopy-based technique in the diagnosis of Neglected Tropical Diseases (NTDs). This paper describes the preliminary studies that are involved in the automated detection of parasitic eggs in a given Kato-katz image. The studies involve application of pattern recognition techniques based on template matching to detect the presence of parasites in the images of kato-katz slides. The results from this study indicate that using a combination of image segmentation and pattern recognition algorithms generates better results as they deal with the complex nature of the images, such as their spurious intensity patterns and shapes. Clinical Relevance— This paper describes interdisciplinary work that applies image processing algorithms to kato-katz images to solve clinical parasitology problems. |
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Keywords | Image classification, Image feature extraction, Image analysis and classification - Digital Pathology |
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Year | 2022 |
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Conference | 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) |
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Publisher | IEEE |
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Accepted author manuscript | |
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Publisher's version | File Access Level Controlled (open metadata, closed files) |
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Publication dates |
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Published | 14 Jun 2022 |
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Web address (URL) of conference proceedings | https://embs.papercept.net/conferences/conferences/EMBC22/program/EMBC22_ContentListWeb_4.html |
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