Towards Low-Cost and Efficient Malaria Detection
- URL: http://arxiv.org/abs/2111.13656v1
- Date: Fri, 26 Nov 2021 18:34:32 GMT
- Title: Towards Low-Cost and Efficient Malaria Detection
- Authors: Waqas Sultani1, Wajahat Nawaz, Syed Javed, Muhammad Sohail Danish,
Asma Saadia, Mohsen Ali
- Abstract summary: Malaria, a fatal but curable disease claims hundreds of thousands of lives every year.
Deep learning-based methods have the potential to decrease the burden of experts but also improve diagnostic accuracy on low-cost microscopes.
We present a dataset to further the research on malaria microscopy over the low-cost microscopes at low magnification.
- Score: 2.7402733069180996
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Malaria, a fatal but curable disease claims hundreds of thousands of lives
every year. Early and correct diagnosis is vital to avoid health complexities,
however, it depends upon the availability of costly microscopes and trained
experts to analyze blood-smear slides. Deep learning-based methods have the
potential to not only decrease the burden of experts but also improve
diagnostic accuracy on low-cost microscopes. However, this is hampered by the
absence of a reasonable size dataset. One of the most challenging aspects is
the reluctance of the experts to annotate the dataset at low magnification on
low-cost microscopes. We present a dataset to further the research on malaria
microscopy over the low-cost microscopes at low magnification. Our large-scale
dataset consists of images of blood-smear slides from several malaria-infected
patients, collected through microscopes at two different cost spectrums and
multiple magnifications. Malarial cells are annotated for the localization and
life-stage classification task on the images collected through the high-cost
microscope at high magnification. We design a mechanism to transfer these
annotations from the high-cost microscope at high magnification to the low-cost
microscope, at multiple magnifications. Multiple object detectors and domain
adaptation methods are presented as the baselines. Furthermore, a partially
supervised domain adaptation method is introduced to adapt the object-detector
to work on the images collected from the low-cost microscope. The dataset will
be made publicly available after publication.
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