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Deep learning approaches for lyme disease detection: leveraging progressive resizing and self-supervised learning models

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Abstract

Lyme disease diagnosis poses a significant challenge, with blood tests exhibiting an alarming inaccuracy rate of nearly 60% in detecting early-stage infections. As a result, there is an urgent need for improved diagnostic methods that can offer more accurate detection outcomes. To address this pressing issue, our study focuses on harnessing the potential of deep learning approaches, specifically by employing model pipelining through progressive resizing and multiple self-supervised learning models. In this paper, we present a comprehensive exploration of self-supervised learning models, including SimCLR, SwAV, MoCo, and BYOL, tailored to the context of Lyme disease detection using medical imaging. The effectiveness and performance of these models are evaluated using standard metrics such as F1 score, precision, recall, and accuracy. Furthermore, we emphasize the significance of progressive resizing and its implications when dealing with convolutional neural networks (CNNs) for medical image analysis. By leveraging deep learning approaches, progressive resizing, and self-supervised learning models, the challenges associated with Lyme disease detection are effectively addressed in this study. The application of our novel methodology and the execution of a comprehensive evaluation framework contribute invaluable insights, fostering the development of more efficient and accurate diagnostic methods for Lyme disease. It is firmly believed that our research will serve as a catalyst, inspiring interdisciplinary collaborations that accelerate progress at the convergence of medicine, computing, and technology, ultimately benefiting public health.

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Data availability

This study uses an open source Lyme Disease Rashes dataset that can be accessed via https://www.kaggle.com/datasets/sshikamaru/lyme-disease-rashes, an open-source online data repository hosted at Kaggle (www.kaggle.com).

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Acknowledgements

The authors would like to thank the Editors and the anonymous reviewers for their valuable comments and suggestions which has helped to improve the quality and clarity of the paper.

Funding

The authors received no funding from an external source.

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Authors and Affiliations

  1. Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), MH, 412115, Pune, India

    Daryl Jacob Jerrish, Om Nankar, Shilpa Gite, Shruti Patil & Ketan Kotecha

  2. Artificial Intelligence and Machine Learning Department, Symbiosis Institute of Technology, Symbiosis International (Deemed) University, MH, 412115, Pune, India

    Daryl Jacob Jerrish, Om Nankar, Shilpa Gite & Shruti Patil

  3. Institute of Actuarial Science and Data Analytics, UCSI University, Jalan Menara Gading, 56000, Cheras, Kuala Lumpur, Malaysia

    Ganeshsree Selvachandran

  4. Symbiosis Institute of Technology Symbiosis International University, Pune, 412115, India

    Ganeshsree Selvachandran

  5. Faculty of Computing and Data Sciences, FLAME University, Gat No. 1270, Lavale, Pune, Maharashtra, 412115, India

    Ajith Abraham

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  1. Daryl Jacob Jerrish
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Contributions

All authors contributed to the conception and design of the study. Material preparation, data collection, data visualization and data analysis were performed by Daryl Jacob Jerrish, Om Nankar, and Shruti Patil. Advanced data analysis and validation were done by Ketan Kotecha, Ganeshsree Selvachandran and Ajith Abraham. Shilpa Gite and Shruti Patil supervised the project, Ketan Kotecha oversaw project administration. The first draft of the manuscript was written by Daryl Jacob Jerrish, Om Nankar, Shilpa Gite and Shruti Patil. The second draft was prepared and edited by Ketan Kotecha, Ganeshsree Selvachandran and Ajith Abraham. The final draft was edited and proof-read by Ganeshsree Selvachandran and Ajith Abraham. All authors commented on previous versions of the manuscript. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Shilpa Gite or Ganeshsree Selvachandran.

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Jerrish, D.J., Nankar, O., Gite, S. et al. Deep learning approaches for lyme disease detection: leveraging progressive resizing and self-supervised learning models. Multimed Tools Appl 83, 21281–21318 (2024). https://doi.org/10.1007/s11042-023-16306-9

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