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Automatic Detection of Eczema Using Image Processing

  • Sakshi Srivastava
  • Abhilasha Singh
  • Ritu Gupta
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 18)

Abstract

Eczema is the most common form of skin disease in humans. Skin diseases like eczema, if not detected and controlled early, may lead to severe health and financial consequences for patients. Most of the skin disease is curable at initial stages with the improvement in technology. Also, an early detection of skin disease can prevent the progression of the disease and save the patient’s life. Early measurement of disease harshness, combined with a recommendation for skin protection and use of appropriate medication, can prevent the disease from worsening. At present, diagnosis can be costly and time-consuming. In this paper, a method for early detection of eczema is presented using modern image processing and algorithms. Techniques such as preprocessing, segmentation, feature extraction, filtering, edge detection, etc. are part of image processing and are used to identify the part affected by disease. Simulation suggests that the proposed system can successfully detect the regions affected by eczema. An attempt has been made to detect eczema-affected region with the help of proposed algorithm.

Keywords

Image processing Morphology Eczema Automatic detection 

References

  1. 1.
    M.N. Alam, T.T.K. Munia, K. Tavakolian, F. Vasefi, N. MacKinnon, R. Fazel-Rezai, Automatic detection and severity index measurement by image processing, in IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 2–5, 2016Google Scholar
  2. 2.
    WebMD, The basics of Eczema and your skin, 2005. [Online]. Available: https://www.webmd.com/skin-problems-and-treatments/guide/atopic-dermatitis-eczema#1
  3. 3.
    S. Arifin, G. Kibria, A. Firoze, A. Amin, H. Yan, Dermatological disease diagnosis using colour-skin images, in International Conference On Machine Learning And Cybernetics, pp. 15–17, 2012Google Scholar
  4. 4.
    T.-T. Do, Y. Zhou, H. Zheng, N.-M. Cheung, D. Koh, Early melanoma diagnosis with mobile imaging. Int. Conf. IEEE Eng. Med. Biol. Soc. 2014, 6752–6757 (2014)Google Scholar
  5. 5.
    P. Rubegni et al., Automated diagnosis on pigmented skin lesions. Int. J. Cancer 101, 576–580 (2002)CrossRefGoogle Scholar
  6. 6.
    A. Karargyris, O. Karargyris, A. Pantelopoulos, DERMA/care: An Advanced image-processing mobile application for monitoring skin cancer, in IEEE 24th International Conference on Tools with Artificial Intelligence, pp. 1–7, 2012Google Scholar
  7. 7.
    L.C. De Guzman, R.P.C. Maglaque, V.M.B. Torres, S.P.A. Zapido, M.O. Cordel, Design and Evaluation of a Multi-model, Multi-level Artificial Neural Network for Eczema Skin Lesion Detection, in Third International Conference on Artificial Intelligence, Modelling and Simulation, pp. 42–47, 2015Google Scholar
  8. 8.
    R. Sumithra, M. Suhil, D. Guru, Segmentation and classification of skin lesions for disease diagnosis. Procedia Comput. Sci. 45, 76–85 (2015)CrossRefGoogle Scholar
  9. 9.
    C. Suter, A. Navarini, M. Pouly, R. Arnold, F.S. Gutzwiller, R. Meier, T. Koller, Detection and quantification of hand eczema by visible spectrum skin pattern analysis. Front. Artif. Intell. Appl. 263, 1101–1102 (2014)Google Scholar
  10. 10.
    J. Roxas, Interview about eczema. Interviewed by Simon Zapido [in person] Robinsons Galleria, 29 Sept 2014Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sakshi Srivastava
    • 1
  • Abhilasha Singh
    • 1
  • Ritu Gupta
    • 1
  1. 1.Amity School of Engineering and Technology, Amity UniversityNoidaIndia

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