Abstract
Leishmaniasis is a commonly neglected disease present in tropical and subtropical countries, affecting 1 billion people. Visceral Leishmaniasis (VL) is the most severe form and can lead to death if left untreated. In this work, we apply deep learning techniques to detect VL in humans through images of slides from the parasitological examination (microscopy) of the bone marrow, aiding in an automatic and accurate diagnosis. This work investigates five deep learning architectures combined with preprocessing, data augmentation, and fine-tuning techniques to detect this disease in images. We compared our results with five related state-of-the-art works, which showed that the proposed classification method surpassed them in all metrics. We achieve an Accuracy of 98.7%, an F1-Score of 98.7%, and a Kappa of 98.7%. Therefore, we demonstrated that trained deep learning models with microscopic slide imaging of bone marrow biological material could precisely help the specialist detect VL in humans.
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Acknowledgements
We thank Dr. Carlos H. N. Costa for the laboratory where we obtained the samples.
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Foundation for Research Support of Piauí (FAPEPI - Notice 02/2021).
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Conceptualization was done by CG, BA, and RS; methodology was done by CG, AL, and RS; formal analysis and investigation were done by CG and RS; writing was done by CG, AL, AR, NA, RV, and RS; funding acquisition was done by RS; resources were done by NA; supervision was done by RS.
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Gonçalves, C., Andrade, N., Borges, A. et al. Automatic detection of Visceral Leishmaniasis in humans using Deep Learning. SIViP 17, 3595–3601 (2023). https://doi.org/10.1007/s11760-023-02585-0
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DOI: https://doi.org/10.1007/s11760-023-02585-0