SpringerLink
Log in

Automated Detection of Infection in Diabetic Foot Ulcer Using Pre-trained Fast Convolutional Neural Network with U++net

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

A frequent consequence of diabetes and a significant contributor to morbidity and mortality is diabetic foot ulcer (DFU).Early detection and appropriate management of DFUs are essential to prevent complications such as infections and lower extremity amputations. In recent years, medical imaging and machine learning have emerged as promising tools for the automated detection and analysis of DFUs. We gathered a sizable foot imaging dataset, including DFU from multiple patients. This paper proposes a novel preprocessing technique based on the Shades of Gray color constancy algorithm to cope with noise and lighting variations in diabetic foot ulcer (DFU) images captured from different devices. The algorithm aims to enhance image quality, improve illumination normalization, and mitigate the impact of noise, thus providing more reliable and accurate DFU analysis and detection. Using the Diabetic Foot Infection Network with the Adam Optimizer (DFINET-AO), features were retrieved after the dataset had been preprocessed and divided. In order to comprehend the normal and pathological spectrum of diabetes, image data and numerical/text data are separated independently. Foot images of patients with aberrant diabetes coverage are separated from each other and classified using Pre-trained Fast Convolutional Neural Network (PFCNN), which has been trained on the U++network. Classification techniques, like foot ulcer analysis, forecast a etiology. This study's primary goal was to establish a novel method for evaluating the likelihood that diabetes individuals may acquire foot ulcers by imaging analysis of existing foot ulcers. The data was preprocessed and segmented after the researchers gathered a collection of foot photographs and medical information from historical records of diabetes patients. The amount of normal and pathological diabetes was then determined from numerical and textual data by extracting characteristics from the segmented data using DFINET-AO. To detect foot ulcers and forecast the chance of diabetic foot ulcer (DFU) formation, foot pictures of patients with aberrant diabetes coverage were pre-trained for rapid convolution using a U++network and classification using a neural network. In this work, we assessed the accuracy of the technique at 99.45% by simulating the diabetic foot ulcer classification and feature extraction outcomes.

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
EUR 32.99 /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
Fig. 12

Availability of Data and Materials

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Parvathala B, Manikandan A, Vijayalakshmi P, Parvez M, Gopalan S, Ramalingam S. Bio-inspired metaheuristic algorithm for network intrusion detection system of architecture; 2024. https://doi.org/10.4018/979-8-3693-5276-2.ch004.

  2. Sheikdavood K, Surendar P, Manikandan A. Certain investigation on latent fingerprint improvement through multi-scale patch based sparse representation. Indian J Eng. 2016;13(31):59–64.

    Google Scholar 

  3. Ali R, Manikandan A, Xu J. A novel framework of adaptive fuzzy-GLCM segmentation and fuzzy with capsules network (F-CapsNet) classification. Neural Comput Applic. 2023. https://doi.org/10.1007/s00521-023-08666-y.

    Article  Google Scholar 

  4. Annamalai M, Muthiah P. An early prediction of tumor in heart by cardiac masses classification in echocardiogram images using robust back propagation neural network classifier. Brazil Arch Biol Technol. 2022. https://doi.org/10.1590/1678-4324-2022210316.

    Article  Google Scholar 

  5. V. A.R, S. David, E. Govinda, K. Ganapriya, R. Dhanapal and A. Manikandan, "An Automatic Brain Tumors Detection and Classification Using Deep Convolutional Neural Network with VGG-19,” 2023 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 2023. pp. 1–5, https://doi.org/10.1109/ICAECA56562.2023.10200949

  6. ManikandanAnnamalai M, Bala Ponni. Intracardiac mass detection and classification using double convolutional neural network classifier. J Eng Res. 2023;11(2A):272–80. https://doi.org/10.36909/jer.12237.

    Article  Google Scholar 

  7. Balamurugan D, Seshadri SA, Reddy P, Rupani A, Manikandan A. Multiview objects recognition using deep learning-based wrap-CNN with Voting scheme. Neural Process Lett. 2022;54:1–27. https://doi.org/10.1007/s11063-021-10679-4.

    Article  Google Scholar 

  8. Palaniappan Mathiyalagan, Annamalai,Manikandan. (2019). Advances in signal and image processing in biomedical applications. https://doi.org/10.5772/intechopen.88759.

  9. Kolli S, et al. Internet of things for pervasive and personalized healthcare: architecture technologies. Compon Appl Prototype Dev. 2023. https://doi.org/10.4018/978-1-6684-8913-0.ch008.

    Article  Google Scholar 

  10. Bommaraju K, Manikandan A, Ramalingam S. Aided system for visually impaired people in bus transport using intel galileo gen-2: technical note. Int J Vehicle Struct Syst. 2017;9(2):110–2. https://doi.org/10.4273/ijvss.9.2.09.

    Article  Google Scholar 

  11. Karpagalakshmi RC, Tensing D. Orientation model for effective event detection in vehicle location. Int J Appl Eng Res. 2014;9:18889–98.

    Google Scholar 

  12. Karpagalakshmi R, Tensing D, Kalpana A. Image localization using deformable model and its application in health informatics. J Med Imaging Health Inf. 2016;6:1972–6. https://doi.org/10.1166/jmihi.2016.1959.

    Article  Google Scholar 

  13. Khandakar A, Chowdhury ME, Reaz MBI, Ali SHM, Hasan MA, Kiranyaz S, Rahman T, Alfkey R, Bakar AAA, Malik RA. A machine learning model for early detection of diabetic foot using thermogram images. Comput Biol Med. 2021;137: 104838.

    Article  Google Scholar 

  14. Ayaz Z, Naz S, Khan NH, Razzak I, Imran M. Automated methods for diagnosis of Parkinson’s disease and predicting severity level. Neural Comput Appl. 2022. https://doi.org/10.1007/s00521-021-06626-y.

    Article  Google Scholar 

  15. Ashraf A, Naz S, Shirazi SH, Razzak I, Parsad M. Deep transfer learning for alzheimer neurological disorder detection. Multimed Tools Appl. 2021;80:30117–42.

    Article  Google Scholar 

  16. Alqaysi, Ziadoon and Shuwandy, Moceheb and Ahmed, Mohammed and Salih, Mamood and Al-Tarazi, Yazan, Multi-Tire CNN Model for Motor Imagery Based UAV Control. Available at SSRN: https://ssrn.com/abstract=4162650 or https://doi.org/10.2139/ssrn.4162650

  17. Manikandan A. A survey on classification of medical images using deep learning. J Image Process Intell Remote Sens. 2021;1(01):5–14. https://doi.org/10.55529/jipirs.11.5.14.

    Article  Google Scholar 

  18. M.A.M., Decision support system for a football team management by using machine learning techniques. **nyang Teachers College. 2018. 10(2): 1–15

  19. Chhabra S, et al. 5G and 6G technologies for smart city. In: Enabling technologies for effective planning and management in sustainable smart cities. Springer; 2023. p. 335–65.

    Chapter  Google Scholar 

  20. Al-Asadi M, Altun AA. Deep learning with SMOTE techniques for improved skin lesion classification on unbalanced data. Selcuk Univ J Eng Sci. 2022;21(3):97–104.

    Google Scholar 

  21. Aiden MK, et al. AI and blockchain for cyber security in cyber-physical system. In: AI models for blockchain-based intelligent networks in IoT systems: concepts, methodologies, tools, and applications. Springer; 2023. p. 203–30.

    Chapter  Google Scholar 

  22. Ali R, Manikandan A, Lei R, et al. A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection. Sci Rep. 2024;14:9336. https://doi.org/10.1038/s41598-024-57393-4.

    Article  Google Scholar 

Download references

Funding

No funding received by any government or private concern.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, SJC Institute of Technology, Chikkaballapur, Karnataka, 562101, India

    S. V. N. Murthy

  2. Information Technology, Aditya College of Engineering and Technology, Surampalem, India

    Kovvuri N. Bhargavi

  3. Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

    Samson Isaac

  4. Department of ECE, St. Peter’s Institute of Higher Education and Research, Chennai, India

    E. N.Ganesh

Authors
  1. S. V. N. Murthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kovvuri N. Bhargavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samson Isaac
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. E. N.Ganesh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. V. N. Murthy.

Ethics declarations

Conflict of Interest

The authors declare that they have no competing interests.

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

Murthy, S.V.N., Bhargavi, K.N., Isaac, S. et al. Automated Detection of Infection in Diabetic Foot Ulcer Using Pre-trained Fast Convolutional Neural Network with U++net. SN COMPUT. SCI. 5, 705 (2024). https://doi.org/10.1007/s42979-024-02981-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-024-02981-4

Keywords

Search

Navigation