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A dataset and benchmark for malaria life-cycle classification in thin blood smear images

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Abstract

Malaria microscopy, microscopic examination of stained blood slides to detect parasitePlasmodium, 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, which results in unavoidable medical complications. 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 classify cells into just healthy or infected. The second stage is trained to classify each detected cell further into the malaria 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 Convolutional Neural Networks 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.

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Notes

  2. The actual magnification is 100\(\times \) 10 (eye-piece).

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Acknowledgements

The project is partially supported by an unrestricted gift award from Facebook, USA. The opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect those of Facebook.

Author information

Authors and Affiliations

  1. Information Technology University, Lahore, Pakistan

    Qazi Ammar Arshad, Mohsen Ali & Waqas Sultani

  2. Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK

    Saeed-ul Hassan

  3. Center for Research in Computer Vision, University of Central Florida, FL, USA

    Chen Chen

  4. Chughtai Institute of Pathology, Lahore, Pakistan

    Ayisha Imran

  5. Ittefaq hospital, Lahore, Pakistan

    Ghulam Rasul

Authors
  1. Qazi Ammar Arshad

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  2. Mohsen Ali

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  3. Saeed-ul Hassan

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  4. Chen Chen

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  5. Ayisha Imran

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  6. Ghulam Rasul

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  7. Waqas Sultani

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Correspondence toWaqas Sultani.

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Arshad, Q.A., Ali, M., Hassan, Su.et al. A dataset and benchmark for malaria life-cycle classification in thin blood smear images.Neural Comput & Applic34, 4473–4485 (2022). https://doi.org/10.1007/s00521-021-06602-6

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