TITLE: A Dataset and Benchmark for Malaria Life-Cycle Classification in Thin Blood Smear Images 

ABSTRACT: Malaria microscopy, microscopic examination of stained blood slides to detect
parasite Plasmodium, is considered to be a gold-standard for detecting
life-threatening disease malaria. Detecting the plasmodium parasite requires a
skilled examiner and may take up to 10 to 15 minutes to completely go through
the whole slide. Due to a lack of skilled medical professionals in the
underdeveloped or resource deficient regions, many cases go misdiagnosed;
resulting in unavoidable complications and/or undue medication. We propose to
complement the medical professionals by creating a deep learning-based method
to automatically detect (localize) the plasmodium parasites in the photograph
of stained film. To handle the unbalanced nature of the dataset, we adopt a
two-stage approach. Where the first stage is trained to detect blood cells and
classify them into just healthy or infected. The second stage is trained to
classify each detected cell further into the life-cycle stage. To facilitate
the research in machine learning-based malaria microscopy, we introduce a new
large scale microscopic image malaria dataset. Thirty-eight thousand cells are
tagged from the 345 microscopic images of different Giemsa-stained slides of
blood samples. Extensive experimentation is performed using different CNN
backbones including VGG, DenseNet, and ResNet on this dataset. Our experiments
and analysis reveal that the two-stage approach works better than the one-stage
approach for malaria detection. To ensure the usability of our approach, we
have also developed a mobile app that will be used by local hospitals for
investigation and educational purposes. The dataset, its annotations, and
implementation codes will be released upon publication of the paper.
Malaria microscopy, microscopic examination of stained blood slides to detect
parasite Plasmodium, is considered to be a gold-standard for detecting
life-threatening disease malaria. Detecting the plasmodium parasite requires a
skilled examiner and may take up to 10 to 15 minutes to completely go through
the whole slide. Due to a lack of skilled medical professionals in the
underdeveloped or resource deficient regions, many cases go misdiagnosed;
resulting in unavoidable complications and/or undue medication. We propose to
complement the medical professionals by creating a deep learning-based method
to automatically detect (localize) the plasmodium parasites in the photograph
of stained film. To handle the unbalanced nature of the dataset, we adopt a
two-stage approach. Where the first stage is trained to detect blood cells and
classify them into just healthy or infected. The second stage is trained to
classify each detected cell further into the life-cycle stage. To facilitate
the research in machine learning-based malaria microscopy, we introduce a new
large scale microscopic image malaria dataset. Thirty-eight thousand cells are
tagged from the 345 microscopic images of different Giemsa-stained slides of
blood samples. Extensive experimentation is performed using different CNN
backbones including VGG, DenseNet, and ResNet on this dataset. Our experiments
and analysis reveal that the two-stage approach works better than the one-stage
approach for malaria detection. To ensure the usability of our approach, we
have also developed a mobile app that will be used by local hospitals for
investigation and educational purposes. The dataset, its annotations, and
implementation codes will be released upon publication of the paper.
A Dataset and Benchmark for Malaria Life-Cycle Classification in Thin Blood Smear Images
http://arxiv.org/abs/2102.08708v1