Skip to main content

Advertisement

Springer Nature Link
Log in

A new deep neuro-fuzzy system for Lyme disease detection and classification using UNet, Inception, and XGBoost model from medical images

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Lyme disease, caused by a bacterium transmitted through the bite of an infected tick, is often misdiagnosed due to its similarity to other conditions like drug rash. This research introduces an innovative approach by integrating prominent deep learning models, including UNet, Inception Model, and XGBoost, into the Deep Neuro-Fuzzy System. Utilizing a comprehensive Kaggle dataset, authors study aims to achieve heightened accuracy in recognizing and segmenting Lyme disease from medical images. Implemented in Python, authors advanced image processing methods demonstrate exceptional performance, reaching an outstanding accuracy of 97.36% after the recognition stage. To further enhance accuracy, authors introduce an additional layer of sophistication through the incorporation of the mayfly optimization (MO) approach. This strategic integration of MO contributes to the outstanding accuracy achieved by their models. This research not only addresses the challenges of Lyme disease misdiagnosis but also presents a robust framework for medical image recognition. Leveraging the collaborative and open nature of Kaggle and the versatility of the Python programming ecosystem, authors work contributes to advancing the field of Lyme disease detection and medical image processing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

All data analyzed during this study are included in this article.

References

  1. Bobe JR, Jutras BL, Horn EJ, Embers ME, Bailey A, Moritz RL, Zhang Y, Soloski MJ, Ostfeld RS, Marconi RT, Aucott J, Ma’ayan A, Keesing F, Lewis K, Ben Mamoun C, Rebman AW, McClune ME, Breitschwerdt EB, Reddy PJ, Maggi R, Yang F, Nemser B, Ozcan A, Garner O, Di Carlo D, Ballard Z, Joung HA, Garcia-Romeu A, Griffiths RR, Baumgarth N, Fallon BA (2021) Recent progress in Lyme disease and remaining challenges. Front Med (Lausanne) 8:666554. https://doi.org/10.3389/fmed.2021.666554

    Article  Google Scholar 

  2. Waddell LA, Greig J, Mascarenhas M, Harding S, Lindsay R, Ogden N (2016) The accuracy of diagnostic tests for Lyme disease in humans, a systematic review and meta-analysis of North American research. PLoS ONE 11(12):e0168613. https://doi.org/10.1371/journal.pone.0168613

    Article  Google Scholar 

  3. Sharma M, Manjari S, Agrawal E, Keshavan P, Koripella R, Majumdar S, Marcinkiewicz A, Lin Y-P, Agrawal R, Banavali N (2023) The structure of a hibernating ribosome in a Lyme disease pathogen. bioRxiv: the preprint server for biology. https://doi.org/10.1101/2023.04.16.537070

  4. Jose T, Pandiaraj S, Velliangiri S (2021) Investigation of smart methodologies in Lyme disease detection. In: 2021 International conference on computer communication and informatics (ICCCI). IEEE, pp 1–5

  5. Scott JD, Foley JE, Anderson JF, Clark KL, Durden LA (2017) Detection of Lyme disease bacterium, Borrelia burgdorferi sensu lato, in blacklegged ticks collected in the Grand River Valley, Ontario, Canada. Int J Med Sci 14(2):150–158. https://doi.org/10.7150/ijms.17763

    Article  Google Scholar 

  6. Eckenrode K (2023) Early identification of Lyme disease complications. JAAPA Off J Am Acad Physician Assist 36:19–23. https://doi.org/10.1097/01.JAA.0000902892.41571.17

    Article  Google Scholar 

  7. Joung HA, Ballard ZS, Wu J, Tseng DK, Teshome H, Zhang L, Horn EJ, Arnaboldi PM, Dattwyler RJ, Garner OB, Di Carlo D, Ozcan A (2020) Point-of-care serodiagnostic test for early-stage Lyme disease using a multiplexed paper-based immunoassay and machine learning. ACS Nano 14(1):229–240. https://doi.org/10.1021/acsnano.9b08151

    Article  Google Scholar 

  8. Palmer J, Ghuman K, Suhail K, Nagib N (2023) Atrial flutter and left hemidiaphragmatic paralysis in the setting of Lyme disease. Cureus. https://doi.org/10.7759/cureus.37374

    Article  Google Scholar 

  9. Ali G, Anwar M, Nauman M, Faheem M, Rashid J (2023) Lyme rashes disease classification using deep feature fusion technique. Skin Res Technol 29:13519. https://doi.org/10.1111/srt.13519

    Article  Google Scholar 

  10. Akbarian S et al (2022) A computer vision approach to identifying ticks related to Lyme disease. IEEE J Transl Eng Health Med 10:1–8. https://doi.org/10.1109/JTEHM.2021.3137956

    Article  Google Scholar 

  11. Forrest I, O’Neal A, Pedra J, Do R (2023) Cholesterol contributes to risk, severity, and machine learning-driven diagnosis of Lyme disease. Clin Infect Dis Off Publ Infect Dis Soc Am. https://doi.org/10.1093/cid/ciad307

    Article  Google Scholar 

  12. Hossain S, de Herve JDG, Hassan MS, Martineau D, Petrosyan E, Corbain V, Beytout J, Lebert I, Baux E, Cazorla C, Eldin C, Hansmann Y, Patrat-Delon S, Prazuck T, Raffetin A, Tattevin P, Vourc'H G, Lesens O, Nguifo EM (2021) Benchmarking convolutional neural networks for diagnosing Lyme disease from images

  13. Jacob D, Nankar O, Gite S, Patil S, Kotecha K (2022) Lyme disease detection using progressive resizing and self-supervised learning algorithmslyme disease detection using progressive resizing and self-supervised learning algorithms

  14. Servellita V, Bouquet J, Rebman A, Yang T, Samayoa E, Miller S, Stone M, Lanteri M, Busch M, Tang P, Morshed M, Soloski M, Aucott J, Chiu C (2022) A diagnostic classifier for gene expression-based identification of early Lyme disease. Commun Med 2:92. https://doi.org/10.1038/s43856-022-00127-2

    Article  Google Scholar 

  15. https://www.kaggle.com/datasets/sshikamaru/lyme-disease-rashes Accessed on 22nd February 2023

  16. Likhitha S, Baskar R (2022) Skin cancer segmentation using R-CNN comparing with inception V3 for better accuracy. In: 2022 11th international conference on system modeling & advancement in research trends (SMART), Moradabad, India. pp 1293–1297. https://doi.org/10.1109/SMART55829.2022.10047686

  17. Carvajal DC, Delgado BM, Ibarra DG, Ariza LC (2022) Skin cancer classification in dermatological images based on a dense hybrid algorithm. In: 2022 IEEE XXIX international conference on electronics, electrical engineering and computing (INTERCON), Lima, Peru. pp 1–4. https://doi.org/10.1109/INTERCON55795.2022.9870129

  18. Rautela K, Kumar D, Kumar V (2022) Detection and localization of breast lesion with VGG19 optimized vision transformer. In: 2022 4th international conference on artificial intelligence and speech technology (AIST), Delhi, India. pp 1–4. https://doi.org/10.1109/AIST55798.2022.10065355

  19. Prasad CR, Arun B, Amulya S, Abboju P, Kollem S, Yalabaka S (2023) Breast cancer classification using CNN with transfer learning models. In: 2023 International conference for advancement in technology (ICONAT), Goa, India. pp 1–5. https://doi.org/10.1109/ICONAT57137.2023.10080148

  20. Hemalatha K, Vetriselvi V (2022) Deep learning based classification of cervical cancer using transfer learning. In: 2022 International conference on electronic systems and intelligent computing (ICESIC), Chennai, India. pp 134–139. https://doi.org/10.1109/ICESIC53714.2022.9783560

  21. Mandala V, Senthilnathan T, Suganyadevi S, Gobhinat S, Selvaraj D, Dhanapal R (2023) An optimized back propagation neural network for automated evaluation of health condition using sensor data. Meas Sens 29:100846

    Article  Google Scholar 

  22. Dhanasekaran S, Silambarasan D, Vivek Karthick P, Sudhakar K (2023) Enhancing pancreatic cancer classification through dynamic weighted ensemble: a game theory approach. Comput Methods Biomech Biomed Eng. https://doi.org/10.1080/10255842.2023.2281277

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Kings Engineering College, Chennai, India

    S. Vishnu Priyan

  2. Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, India

    S. Dhanasekaran

  3. Department of Electronics and Communication Engineering, Sona College of Technology, Salem, Tamil Nadu, India

    P. Vivek Karthick

  4. Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Chennai, Tamil Nadu, India

    D. Silambarasan

Authors
  1. S. Vishnu Priyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Dhanasekaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Vivek Karthick
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. D. Silambarasan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Vishnu Priyan.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Priyan, S.V., Dhanasekaran, S., Karthick, P.V. et al. A new deep neuro-fuzzy system for Lyme disease detection and classification using UNet, Inception, and XGBoost model from medical images. Neural Comput & Applic 36, 9361–9374 (2024). https://doi.org/10.1007/s00521-024-09583-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-024-09583-4

Keywords