Skip to main content
SpringerLink
Log in

Severe Acne Skin Disease: A Fuzzy-Based Method for Diagnosis

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12254))

Included in the following conference series:

Abstract

Dermis ailments are disorders that hurt or damage the dermis that has an enormous impact on the everyday life of a person. People’s tight schedule has significantly impacted their accessibility for repetitive examinations, thereby preventing individuals from consulting a medical practitioner. Network-centered medicinal schemes’ popularity is increasingly becoming a model for helping individuals recognize how crucial the level of an ailment is. Acne dermis ailment is one of the extremely well-known dermis sicknesses that troubles the sebaceous glands, thus repetitive diagnosis could assist to avoid blisters. Fuzzy based approach for diagnosing acne skin disease was proposed in this paper. It was suggested that the approach assisted solving the shortcomings of previous expert system methods. Expert machine reasoning is related to literary ambiguity. The proposed system of used fuzzy rules to address inaccuracy in the expert system’s analysis. It was proven that the scheme was 82% accurate, indicating good performance. The Fuzzy expert system built had shown an extreme level of guidance, medical care recommendations and demonstrated the degree of seriousness of acne dermis state in patients.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ak, M.: A comprehensive review of acne vulgaris. J. Clin. Pharm. 1(1), 17–45 (2019)

    Google Scholar 

  2. Pereira, M.P., Kremer, A.E., Mettang, T., Ständer, S.: Chronic pruritus in the absence of skin disease: pathophysiology, diagnosis and treatment. Am. J. Clin. Dermatol. 1–12. Clerk Maxwell, J.: A Treatise on Electricity and Magnetism, 3rd edn., vol. 2. Oxford: Clarendon, 1892, 2016, pp. 68–73

    Google Scholar 

  3. Adeyinka, A.A., Adebiyi, M.O., Akande, N.O., Ogundokun, R.O., Kayode, A.A., Oladele, T.O.: A deep convolutional encoder-decoder architecture for retinal blood vessels segmentation. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11623, pp. 180–189. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24308-1_15

    Chapter  Google Scholar 

  4. Habif, T.P., Chapman, M.S., Campbell Jr., J.L., Dinulos, J.G., Zug, K.A.: Skin disease: diagnosis and treatment. Elsevier Health Sciences, pp. 201–214 (2011)

    Google Scholar 

  5. Yadav, G., Pandey, G.N.: Development of intelligent decision and prediction system using cyber-enabled. NESS Technology for oil availability and yield prediction. RIET-IJSET: Int. J. Sci. Eng. Technol. 2(1), 41–52 (2015)

    Google Scholar 

  6. Amarathunga, A.A.L.C., Ellawala, E.P.W.C., Abeysekara, G.N., Amalraj, C.R.J.: Expert system for diagnosis of skin diseases. Int. J. Sci. Technol. Res. 4(1), 456–472 (2015)

    Google Scholar 

  7. Lingiardi, V., McWilliams, N., Bornstein, R.F., Gazzillo, F., Gordon, R.M.: The psychodynamic diagnostic manual version 2 (PDM-2): assessing patients for improved clinical practice and research. Psychoanalytic Psychol. 32(1), 94 (2015)

    Article  Google Scholar 

  8. Moses, D.: A survey of data mining algorithms used in cardiovascular disease diagnosis from multi-lead ECG data. Kuwait J. Sci. 42(2) (2015)

    Google Scholar 

  9. Adegun, A.A., Akande, N.O., Ogundokun, R.O., Asani, E.O.: Image segmentation and classification of large scale satellite imagery for land use: a review of the state of the arts. Int. J. Civil Eng.Technol. 9(11) (2018)

    Google Scholar 

  10. Ajala, F.A., Akande, N.O., Adeyemo, I.A., Ogundokun, R.O.: Smallest univalue segment assimilating nucleus approach to brain MRI image segmentation using fuzzy C-means and fuzzy K-means algorithms. Int. J. Comput. Technol. 16(7), 7065–7076 (2017)

    Article  Google Scholar 

  11. Ayo, F.E., Awotunde, J.B., Ogundokun, R.O., Folorunso, S.O., Adekunle, A.O.: A decision support system for multi-target disease diagnosis: a bioinformatics approach. Heliyon 6(3), e03657 (2020)

    Article  Google Scholar 

  12. McPherson, R.A., Pincus, M.R.: Henry’s clinical diagnosis and management by laboratory methods. Elsevier Health Sciences (2016)

    Google Scholar 

  13. Jimoh, R.G., Awotunde, J.B., Babatunde, A.O., Ameen, A.O, James, T.R., Fatai, O.W.: Simulation of medical diagnosis system for malaria using fuzzy logic. Int. J. Inf. Process. Commun. (IJIPC) 2(1) (2014). Published by Faculty of Communication and Information Sciences, University of Ilorin, Ilorin

    Google Scholar 

  14. Horvitz, E.J., Breese, J.S., Henrion, M.: Decision theory in expert systems and artificial intelligence. Int. J. Approximate Reasoning 2(3), 247–302 (1988)

    Article  Google Scholar 

  15. Constantinou, A.C., Fenton, N., Marsh, W., Radlinski, L.: From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. Artif. Intell. Med. 67, 75–93 (2016)

    Article  Google Scholar 

  16. Ayo, F.E., et al.: A fuzzy based method for diagnosis of acne skin disease severity. i-manager’s. J. Pattern Recogn. 5(2), 10 (2018)

    Google Scholar 

  17. Adebayo, O., Asani, E.O., Ogundokun, R.O., Ananti, E.C., Adegun, A.: A neuro-fuzzy based system for the classification of cells as cancerous of non-cancerous. Int. J. Med. Res. Health Sci. 7(5), 155–166 (2018)

    Google Scholar 

  18. Thompson, T., Sowunmi, O., Misra, S., Fernandez-Sanz, L., Crawford, B., Soto, R.: An expert system for the diagnosis of sexually transmitted diseases–ESSTD. J. Intell. Fuzzy Syst. 33(4), 2007–2017 (2017)

    Article  Google Scholar 

  19. Jimoh, R.G., Afolayan, A.A., Awotunde, J.B., Matiluko, E.O.: Fuzzy logic based expert system in the diagnosis of ebola virus. Ilorin J. Comput. Sci. Inf. Technol. 2(1), 73–94 (2017). Published by Computer Science Department, University of Ilorin, Ilorin, Nigeria

    Google Scholar 

  20. Azeez, N.A., Towolawi, T., Van der Vyver, C., Misra, S., Adewumi, A., Damaševičius, R., Ahuja, R.: A fuzzy expert system for diagnosing and analyzing human diseases. In: Abraham, A., Gandhi, N., Pant, M. (eds.) IBICA 2018. AISC, vol. 939, pp. 474–484. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16681-6_47

    Chapter  Google Scholar 

  21. Lawanya Shri, M., Ganga Devi, E., Balusamy, B., Kadry, S., Misra, S., Odusami, M.: A fuzzy based hybrid firefly optimization technique for load balancing in cloud datacenters. In: Abraham, A., Gandhi, N., Pant, M. (eds.) IBICA 2018. AISC, vol. 939, pp. 463–473. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16681-6_46

    Chapter  Google Scholar 

  22. Alhassan, J.K., Misra, S., Umar, A., Maskeliūnas, R., Damaševičius, R., Adewumi, A.: A fuzzy classifier-based penetration testing for web applications. In: Rocha, Á., Guarda, T. (eds.) ICITS 2018. AISC, vol. 721, pp. 95–104. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73450-7_10

    Chapter  Google Scholar 

  23. Adegun, A.A., Ogundokun, R.O., Adebiyi, M.O., Asani, E.O.: CAD-based machine learning project for reducing human-factor-related errors in medical image analysis. In: Misra, S., Adewumi, A. (eds.) Handbook of Research on the Role of Human Factors in IT Project Management, pp. 164–172. IGI Global, Hershey (2020). https://doi.org/10.4018/978-1-7998-1279-1.ch011

  24. Awotunde, J.B., Matiluko, O.E., Fatai, O.W.: Medical diagnosis system using fuzzy logic. Afr. J. Comput. ICT 7(2), 99–106 (2014). Published by IEEE Computer Society, Nigeria Section

    Google Scholar 

  25. Jang, J.S.: Neuro-Fuzzy Modeling: Architectures, Analyses, and Applications. University of California, Berkeley (1992)

    Google Scholar 

  26. Bo, Y. (ed.): Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A Zadeh, vol. 6. World Scientific (1996)

    Google Scholar 

  27. Hachaj, T., Ogiela, M.R.: Rule-based approach to recognizing human body poses and gestures in real time. Multimedia Syst. 20(1), 81–99 (2013). https://doi.org/10.1007/s00530-013-0332-2

    Article  Google Scholar 

  28. Alamdari, N., Tavakolian, K., Alhashim, M., Fazel-Rezai, R.: Detection and classification of acne lesions in acne patients: a mobile application. In: 2016 IEEE International Conference on Electro Information Technology (EIT), pp. 0739–0743. IEEE, May 2016

    Google Scholar 

  29. Zouboulis, C.C., Del Marmol, V., Mrowietz, U., Prens, E.P., Tzellos, T., Jemec, G.B.: Hidradenitis suppurativa/acne inversa: criteria for diagnosis, severity assessment, classification and disease evaluation. Dermatology 231(2), 184–190 (2015)

    Article  Google Scholar 

  30. Asghar, M.Z., Asghar, M.J., Saqib, S.M., Ahmad, B., Ahmad, S., Ahmad, H.: Diagnosis of skin diseases using online expert system. Int. J. Comput. Sci. Inf. Secur. 9(6), 323 (2011)

    Google Scholar 

  31. Putra, A.A., Munir, R.: Implementation of fuzzy inference system in children skin disease diagnosis application. In: 2015 International Conference on Electrical Engineering and Informatics (ICEEI), pp. 365–370. IEEE, August 2015

    Google Scholar 

  32. Aruta, C.L., Calaguas, C.R., Gameng, J.K., Prudentino, M.V., Anthony, A., Lubaton, C.J.: Mobile-based medical assistance for diagnosing different types of skin diseases using case-based reasoning with image processing. IJ CI 3(3), 115–118 (2015)

    Google Scholar 

  33. Shen, X., Zhang, J., Yan, C., Zhou, H.: An automatic diagnosis method of facial acne vulgaris based on convolutional neural network. Sci. Rep. 8(1), 1–10 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Physical and Computer Sciences, McPherson University, Seriki-Sotayo, Abeokuta, Nigeria

    Femi Emmanuel Ayo

  2. Department of Computer Science, Landmark University, Omu-Aran, Nigeria

    Roseline Oluwaseun Ogundokun, Marion Olubunmi Adebiyi & Abidemi Emmanuel Adeniyi

  3. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Joseph Bamidele Awotunde

Authors
  1. Femi Emmanuel Ayo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roseline Oluwaseun Ogundokun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joseph Bamidele Awotunde
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marion Olubunmi Adebiyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abidemi Emmanuel Adeniyi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roseline Oluwaseun Ogundokun .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ayo, F.E., Ogundokun, R.O., Awotunde, J.B., Adebiyi, M.O., Adeniyi, A.E. (2020). Severe Acne Skin Disease: A Fuzzy-Based Method for Diagnosis. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham. https://doi.org/10.1007/978-3-030-58817-5_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58817-5_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58816-8

  • Online ISBN: 978-3-030-58817-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation