PlasmoID: A dataset for Indonesian malaria parasite detection and
segmentation in thin blood smear
- URL: http://arxiv.org/abs/2211.15105v1
- Date: Mon, 28 Nov 2022 07:47:09 GMT
- Title: PlasmoID: A dataset for Indonesian malaria parasite detection and
segmentation in thin blood smear
- Authors: Hanung Adi Nugroho, Rizki Nurfauzi, E. Elsa Herdiana Murhandarwati,
Purwono Purwono
- Abstract summary: Indonesia holds the second-highest-ranking country for the highest number of malaria cases in Southeast Asia.
A different malaria parasite semantic segmentation technique based on a deep learning approach is an alternative to reduce the limitations of traditional methods.
This study proposes a malaria parasite segmentation and detection scheme by combining Faster RCNN and a semantic segmentation technique.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Indonesia holds the second-highest-ranking country for the highest number of
malaria cases in Southeast Asia. A different malaria parasite semantic
segmentation technique based on a deep learning approach is an alternative to
reduce the limitations of traditional methods. However, the main problem of the
semantic segmentation technique is raised since large parasites are dominant,
and the tiny parasites are suppressed. In addition, the amount and variance of
data are important influences in establishing their models. In this study, we
conduct two contributions. First, we collect 559 microscopic images containing
691 malaria parasites of thin blood smears. The dataset is named PlasmoID, and
most data comes from rural Indonesia. PlasmoID also provides ground truth for
parasite detection and segmentation purposes. Second, this study proposes a
malaria parasite segmentation and detection scheme by combining Faster RCNN and
a semantic segmentation technique. The proposed scheme has been evaluated on
the PlasmoID dataset. It has been compared with recent studies of semantic
segmentation techniques, namely UNet, ResFCN-18, DeepLabV3, DeepLabV3plus and
ResUNet-18. The result shows that our proposed scheme can improve the
segmentation and detection of malaria parasite performance compared to original
semantic segmentation techniques.
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