Abstract
Recent studies use machine-learning techniques to automatically detect parasites in microscopy images. However, these tools are trained and tested in specific datasets. Indeed, even if over-fitting is avoided during the improvements of computer vision applications, large differences are expected. Differences might be related to settings of camera (exposure, white balance settings, etc.) and different blood film slides preparation. Generative adversial networks offer new opportunities in microscopy: data homogenization, and increase of images in case of imbalanced or small sample size. Taking into consideration all those aspects, in this paper, we describe a more complete view including both detection and generating synthetic images: i) an automated detection used to detect malaria parasites on stained blood smear images using machine learning techniques testing several datasets. ii) a generative approach to create synthetic images which can deceive experts, and provide new data for data transfer or augmentation. The tested architecture achieved 0.98 and 0.95 area under the ROC curve in classifying images with respectively thin and thick smear, in leave-one-out cross-validation settings. The generated images proved to be very similar to the original and difficult to be distinguished by an expert microscopist, which identified correctly the real data for one dataset but had 50% misclassification for another dataset of images. Moveover, the trained model was also tested on a low cost device as RaspberryPI 4 with display.
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Ramarolahy, R.T.C., Gyasi, E.O., Crimi, A. (2021). Classification and Generation of Microscopy Images with Plasmodium Falciparum via Artificial Neural Networks Using Low Cost Settings. In: Albarqouni, S., et al. Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health. DART FAIR 2021 2021. Lecture Notes in Computer Science(), vol 12968. Springer, Cham. https://doi.org/10.1007/978-3-030-87722-4_14
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DOI: https://doi.org/10.1007/978-3-030-87722-4_14
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