Skip to main content
SpringerLink
Log in

Optimized vision transformer encoder with cnn for automatic psoriasis disease detection

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Psoriasis is a skin disorder that results in swollen skin cells and red, itchy areas on the skin. 40% of the world's population is currently affected by psoriasis. Nowadays, using skin image analysis technology is the main way for detecting psoriasis. Additionally, a number of academics have identified potential machine learning methods for categorising the psoriasis illness. However, the accuracy and computational efficiency of the model still need to be improved. Thus, in this paper, we present an optimized vision transformer for autonomous psoriasis disease detection. Following pre-processing, feature optimized image is attained using convolutional neural network (CNN) which embeds full image and concatenates to each vision transformer encoder layer. It leads the network to always “retain” the full image at the end of each transformer block output. In parallel, the pre-processed images are cropped into patches and these patches along with its positional encoded information are given as input to the optimized transformer encoder. To enhance the performance of transformer, the hyper-parameters of it are optimized using adaptive rabbit optimization algorithm (AROA). Results of this article confirm that the proposed optimized vision transformer model achieved better classification accuracy of 97.7% and F-Score of 96.5%.

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

Access this article

Log in via an institution

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

Data availability

Data sharing is not applicable to this article because of proprietary nature.

Code availability

Code sharing is not applicable to this article because of proprietary nature.

References

  1. Raj R, Londhe ND, Sonawane RS (2020) Automatic psoriasis lesion segmentation from raw color images using deep learning. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (723–728)

  2. Pasch MC (2016) Nail psoriasis: a review of treatment options. Drugs 76(6):675–705

    Article  Google Scholar 

  3. Negrei C, Boda D (2017) The role of methotrexate in psoriatic therapy in the age of biologic and biosimilar medication: Therapeutic Benefits versus Toxicology Emergencies. Psoriasis: An Interdisciplinary Approach to 211

  4. Raina A, Hennessy R, Rains M, Allred J, Hirshburg JM, Diven DG, Markey MK (2016) Objective measurement of erythema in psoriasis using digital color photography with color calibration. Skin Res Technol 22(3):375–380

    Article  Google Scholar 

  5. George Y, Aldeen M, Garnavi R (2016) Pixel-based skin segmentation in psoriasis images. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (1352–1356)

  6. Geale K, Henriksson M, Schmitt-Egenolf M (2017) How is disease severity associated with quality of life in psoriasis patients? Evidence from a longitudinal population-based study in Sweden. Health Qual Life Outcomes 15:1–9

    Article  Google Scholar 

  7. Monga V, Li Y, Eldar YC (2021) Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing. IEEE Signal Process Mag 38(2):18–44

    Article  Google Scholar 

  8. Lore KG, Akintayo A, Sarkar S (2017) LLNet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recogn 61:650–662

    Article  Google Scholar 

  9. Rezaee M, Mahdianpari M, Zhang Y, Salehi B (2018) Deep convolutional neural network for complex wetland classification using optical remote sensing imagery. IEEE J Sel Top Appl Earth Observations Remote Sens 11(9):3030–3039

    Article  Google Scholar 

  10. Yang C, Jiang W, Guo Z (2019) Time series data classification based on dual path CNN-RNN cascade network. IEEE Access 7:155304–155312

    Article  Google Scholar 

  11. Peng LI, Na YI, Changsong D, Sheng LI, Hui M (2021) Research on classification diagnosis model of psoriasis based on deep residual network. Digital Chin Med 4(2):92–101

    Article  Google Scholar 

  12. Ahammed M, Al Mamun M, Uddin MS (2022) A machine learning approach for skin disease detection and classification using image segmentation. Healthcare Analytics 2:100122

    Article  Google Scholar 

  13. Sadik R, Majumder A, Biswas AA, Ahammad B, Rahman MM (2023) An in-depth analysis of Convolutional Neural Network architectures with transfer learning for skin disease diagnosis. Healthcare Analytics 3:100143

    Article  Google Scholar 

  14. Balaji VR, Suganthi ST, Rajadevi R, Kumar VK, Balaji BS, Pandiyan S (2020) Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier. Measurement 163:107922

    Article  Google Scholar 

  15. Roslan RB, Razly INM, Sabri N, Ibrahim Z (2020) Evaluation of psoriasis skin disease classification using convolutional neural network. IAES Int J Artif Intell 9(2):349

    Google Scholar 

  16. Aijaz SF, Khan SJ, Azim F, Shakeel CS, Hassan U (2022) Deep learning application for effective classification of different types of psoriasis. J Healthcare Eng 2022:12

    Article  Google Scholar 

  17. Dash M, Londhe ND, Ghosh S, Semwal A, Sonawane RS (2019) PsLSNet: Automated psoriasis skin lesion segmentation using modified U-Net-based fully convolutional network. Biomed Signal Process Control 52:226–237

    Article  Google Scholar 

  18. Kumar VB, Kumar SS, Saboo V (2016) Dermatological disease detection using image processing and machine learning. In: 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) (1–6)

  19. Wang L, Cao Q, Zhang Z, Mirjalili S, Zhao W (2022) Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 114:105082

    Article  Google Scholar 

  20. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In international conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC'06) (1:695–701)

  21. Hussein SA, Elhefny AM, Abdulrahman MA, Aziz NN (2021) Early detection of subclinical lower limb enthesopathy by ultrasonography in patients with psoriasis: Relation to disease severity. Egyptian Rheumatol 43(2):153–157

    Article  Google Scholar 

  22. Erfan R, Shaker OG, Khalil MAF, AlOrbani AM, Abu-El-Azayem AK, Samy A, Zaki OM, Abdelhamid H, Fares R, Mohammed A (2023) Lnc-HULC, miR-122, and sirtulin-1 as potential diagnostic biomarkers for psoriasis and their association with the development of metabolic syndrome during the disease course. Non-coding RNA Res 8(3):340–349

    Article  Google Scholar 

  23. Dash M, Londhe ND, Ghosh S, Shrivastava VK, Sonawane RS (2020) Swarm intelligence based clustering technique for automated lesion detection and diagnosis of psoriasis. Comput Biol Chem 86:107247

    Article  Google Scholar 

  24. ElMallah R (2020) Nail affection as a central part of the entheseal organ in psoriasis patients for early detection of psoriatic arthritis. The Egyptian Rheumatol 42(4):319–324

    Article  Google Scholar 

  25. Shtanko A, Kulik S (2022) Preliminary experiments on psoriasis classification in images. Procedia Computer Sci 213:250–254

    Article  Google Scholar 

Download references

Funding

The authors declare that they have competing interests and funding.

Author information

Authors and Affiliations

  1. Computer Science & Engineering, Indian Institute of Information Technology, Bhopal, MP, India

    Gagan Vishwakarma

  2. Computer Science & Engineering, Maulana Azad National Institute of Technology, Bhopal, MP, India

    Amit Kumar Nandanwar

  3. Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, MP, India

    Ghanshyam Singh Thakur

Authors
  1. Gagan Vishwakarma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amit Kumar Nandanwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ghanshyam Singh Thakur
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors read and approved the final manuscript.

Corresponding author

Correspondence to Amit Kumar Nandanwar.

Ethics declarations

Conflict of interests

On behalf of all authors, the corresponding author states that there is 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

Vishwakarma, G., Nandanwar, A.K. & Thakur, G.S. Optimized vision transformer encoder with cnn for automatic psoriasis disease detection. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-16871-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-023-16871-z

Keywords

Search

Navigation