Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Jun 1;24(1):551.
doi: 10.1186/s12879-024-09428-4.

A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine

Alireza Sadeghi  1 , Mahdieh Sadeghi  2 , Mahdi Fakhar  3 , Zakaria Zakariaei  4 , Mohammadreza Sadeghi  5 , Reza Bastani  6
Affiliations

A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine

Alireza Sadeghi et al. BMC Infect Dis. .

Abstract

Background: Leishmaniasis, an illness caused by protozoa, accounts for a substantial number of human fatalities globally, thereby emerging as one of the most fatal parasitic diseases. The conventional methods employed for detecting the Leishmania parasite through microscopy are not only time-consuming but also susceptible to errors. Therefore, the main objective of this study is to develop a model based on deep learning, a subfield of artificial intelligence, that could facilitate automated diagnosis of leishmaniasis.

Methods: In this research, we introduce LeishFuNet, a deep learning framework designed for detecting Leishmania parasites in microscopic images. To enhance the performance of our model through same-domain transfer learning, we initially train four distinct models: VGG19, ResNet50, MobileNetV2, and DenseNet 169 on a dataset related to another infectious disease, COVID-19. These trained models are then utilized as new pre-trained models and fine-tuned on a set of 292 self-collected high-resolution microscopic images, consisting of 138 positive cases and 154 negative cases. The final prediction is generated through the fusion of information analyzed by these pre-trained models. Grad-CAM, an explainable artificial intelligence technique, is implemented to demonstrate the model's interpretability.

Results: The final results of utilizing our model for detecting amastigotes in microscopic images are as follows: accuracy of 98.95 1.4%, specificity of 98 2.67%, sensitivity of 100%, precision of 97.91 2.77%, F1-score of 98.92 1.43%, and Area Under Receiver Operating Characteristic Curve of 99 1.33.

Conclusion: The newly devised system is precise, swift, user-friendly, and economical, thus indicating the potential of deep learning as a substitute for the prevailing leishmanial diagnostic techniques.

Keywords: Artificial intelligence; Deep learning; Image processing; Leishmania; Machine learning; Microscopic images; Transfer learning.

PubMed Disclaimer

Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Fig. 1
Fig. 1
The entire preprocessing pipeline applied to the microscopic images utilized in this study. The initial training set is expanded through random zooming and random rotation of the images within it
Fig. 2
Fig. 2
Integration of a new head across four diverse pretrained models, replacing their original heads, for binary classification tasks classifying CT scans into COVID-19 and normal classifications
Fig. 3
Fig. 3
The architecture of LeishFuNet, leveraging the fusion of information extracted by four medical pre-trained models
Fig. 4
Fig. 4
Performance of LeishFuNet during training. (a) Shows changes in loss values, while (b) illustrates changes in accuracy values throughout the training process
Fig. 5
Fig. 5
Implementing Grad-CAM on LeishFuNet involves calculating gradients of LeishFuNet’s output score with respect to the output of the second-to-last convolutional layer
Fig. 6
Fig. 6
a) Analyzed regions of the microscopic images by LeishFuNet, leading the model to classify these images as Leishmania positive. b) Analyzed regions of low-density Leishmania parasite images by LeishFuNet for prediction. c) Analyzed regions of negative Leishmania parasite images by LeishFuNet for prediction
Fig. 7
Fig. 7
Changes in a) loss values and b) accuracy during the training of LeishFuNet on the microscopic images of parasites species dataset
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Hotez PJ, Aksoy S, Brindley PJ, Kamhawi S. World neglected tropical diseases day. In., vol. 14: Public Library of Science; 2020: e0007999. - PMC - PubMed
    1. Ahmed A, Aune D, Vineis P, Pescarini JM, Millett C, Hone T. The effect of conditional cash transfers on the control of neglected tropical disease: a systematic review. Lancet Global Health. 2022;10(5):e640–8. doi: 10.1016/S2214-109X(22)00065-1. - DOI - PubMed
    1. Souto EB, Dias-Ferreira J, Craveiro SA, Severino P, Sanchez-Lopez E, Garcia ML, Silva AM, Souto SB, Mahant S. Therapeutic interventions for countering leishmaniasis and chagas’s disease: from traditional sources to nanotechnological systems. Pathogens. 2019;8(3):119. doi: 10.3390/pathogens8030119. - DOI - PMC - PubMed
    1. Lin Y, Fang K, Zheng Y, Wang H-l, Wu J. Global burden and trends of neglected tropical diseases from 1990 to 2019. J Travel Med 2022, 29(3). - PubMed
    1. Valero NNH, Prist P, Uriarte M. Environmental and socioeconomic risk factors for visceral and cutaneous leishmaniasis in São Paulo, Brazil. Sci Total Environ. 2021;797:148960. doi: 10.1016/j.scitotenv.2021.148960. - DOI - PubMed