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DSCN-net: a deep Siamese capsule neural network model for automatic diagnosis of malaria parasites detection

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Abstract

The epidemic of malaria has caused many deaths for decades around the world. To shorten the morbidity of malaria, accurate and fast diagnostic tools are to be implied with Artificial Intelligence. In this research, an automated, fast, and accurate diagnostic model for one-shot detection and classification of malaria thin blood smears were developed using the Deep Siamese Capsule Network (D-SCN) and contributions for our research are threefold. Firstly, we proposed the D-SCN model which consists of two crucial phases which are feature extraction and feature discrimination. Secondly, we implied an end-to-end trained capsule network with an imperative routing (IR) mechanism for the feature extraction phase to capture feature invariances. Finally, at the feature discrimination phase, Lorentz, L1 and L2 similarity metrics were proposed for the dissimilation of features. During experimentation, it is observed that the Lorentz similarity metric provided more discriminative capability by acquiring the least MSE at most of the instances. Further, an algorithm is proposed to obtain faster convergence by cautiously tuning the hyperparameters, and this aided in decreasing the noise scale in training the D-SCN. The experimental outcomes proved that D-SCN outperformed with the highest detection accuracy of 97.24% for Lorentz as a similarity measure, and the Capsule network with IR mechanism outperformed with the highest classification accuracy of 98.89%. To our knowledge, the proposed research implications are the first applications of D-SCN for one-shot detection and classification of thin blood smears with state-of-the-art performance.

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Acknowledgments

We thank the JNTUH-TEQIP-III team for providing a grant for the current research with proceedings number: JNTUH/TEQIP-III/2019/CSC/13. We are grateful to the Department of Information Technology, VNRVJIET, for providing extensive support during research.

Availability of data

The data that support the findings of this study are available from the first author upon reasonable request.

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The code is available from the first author upon reasonable request.

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Authors and Affiliations

  1. Department of Information Technology, VNRVJIET, Hyderabad, 500090, India

    G. Madhu

  2. Department of Computer Science and Engineering, JNTUH College of Engineering, 85, T.S, Hyderabad, India

    A. Govardhan

  3. Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia

    Vinayakumar Ravi

  4. LBEF Campus, Kathmandu, 44600, Nepal

    Sandeep Kautish

  5. Department of Computer Science and Engineering, TKR College of Engineering & Technology, 97, T.S, Hyderabad, India

    B. Sunil Srinivas

  6. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

    Tanupriya Chaudhary & Manoj Kumar

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  1. G. Madhu
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Correspondence to Manoj Kumar.

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Madhu, G., Govardhan, A., Ravi, V. et al. DSCN-net: a deep Siamese capsule neural network model for automatic diagnosis of malaria parasites detection. Multimed Tools Appl 81, 34105–34127 (2022). https://doi.org/10.1007/s11042-022-13008-6

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  • DOI: https://doi.org/10.1007/s11042-022-13008-6

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