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A dataset and benchmark for malaria life-cycle classification in thin blood smear images
- Source :
- Neural Computing and Applications. 34:4473-4485
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
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.
- Subjects :
- FOS: Computer and information sciences
Computer science
business.industry
Computer Vision and Pattern Recognition (cs.CV)
Deep learning
Image and Video Processing (eess.IV)
Computer Science - Computer Vision and Pattern Recognition
Plasmodium parasite
Usability
Pattern recognition
Gold standard (test)
Electrical Engineering and Systems Science - Image and Video Processing
medicine.disease
Convolutional neural network
Blood smear
Artificial Intelligence
parasitic diseases
FOS: Electrical engineering, electronic engineering, information engineering
Benchmark (computing)
medicine
Artificial intelligence
business
Software
Malaria
Subjects
Details
- ISSN :
- 14333058 and 09410643
- Volume :
- 34
- Database :
- OpenAIRE
- Journal :
- Neural Computing and Applications
- Accession number :
- edsair.doi.dedup.....6cd6ac4e3bd09016199d3c8af8a5b83e