Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
MRI Brain Tumor Segmentation Using Bidimensional Empirical Mode Decomposition and Morphological Operations Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Detection and Diagnosis of Atopic Dermatitis Using Deep Learning Network

Authors : Anh-Minh Nguyen, Van-Hieu Vu, Thanh-Binh Trinh

Published in: Intelligent Systems and Networks

Publisher: Springer Nature Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper proposes a method to diagnose atopic dermatitis based on deep learning network by analyzing image data of infected skin areas. We use deep learning network to analyze the layers and get the suitable layer for diseased and non-diseased images. These images are further feature extracted through HOG feature extraction and SVM classifier to classify disease and non-disease. The proposed method proves to be effective in precision and recall, that can be considered as an adjunct to traditional diagnostic methods, and the results obtained are equivalent to that of a diagnostician while limiting the heterogeneity between the predictors.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Power Management for Distributed DC Microgrid Under Minimum System Price
next chapter Prediction of the Welding Process Parameters and the Weld Bead Geometry for Robotic Welding Applications with Adaptive Neuro-Fuzzy Models
Literature
1.
Aoki, T., Fukuzumi, T., Adachi, J., Endo, K., Kojima, M.: Re-evaluation of skin lesion distribution in atopic dermatitis. Analysis of cases 0 to 9 years of age. Acta Derm. Venereol. Suppl. 176, 19–23 (1992)
2.
Larsen, F.S., Hanifin, J.M.: Epidemiology of atopic dermatitis. Immunol. Allergy Clin. 22(1), 1–24 (2002)
3.
Hong, S., et al.: The prevalence of atopic dermatitis, asthma, and allergic rhinitis and the comorbidity of allergic diseases in children. Environ. Health Toxicol. 27 (2012)
4.
Fuxench, Z.C.C., et al.: Atopic dermatitis in America study: a cross-sectional study examining the prevalence and disease burden of atopic dermatitis in the us adult population. J. Investig. Dermatol. 139(3), 583–590 (2019)CrossRef
5.
Luoma, R., Koivikko, A., Viander, M.: Development of asthma, allergic rhinitis and atopic dermatitis by the age of five years: a prospective study of 543 newborns. Allergy 38(5), 339–346 (1983)CrossRef
6.
Silverberg, J., et al.: Symptoms and diagnosis of anxiety and depression in atopic dermatitis in us adults. Br. J. Dermatol. 181(3), 554–565 (2019)
7.
Medela, A., Mac Carthy, T., Robles, S.A.A., Chiesa-Estomba, C.M., Grimalt, R.: Automatic scoring of atopic dermatitis using deep learning: a pilot study. JID Innov. 2(3), 100107 (2022)
8.
Wang, S., Yang, D.M., Rong, R., Zhan, X., Xiao, G.: Pathology image analysis using segmentation deep learning algorithms. Am. J. Pathol. 189(9), 1686–1698 (2019)CrossRef
9.
Mazurowski, M.A., Buda, M., Saha, A., Bashir, M.R.: Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI. J. Magn. Reson. Imaging 49(4), 939–954 (2019)CrossRef
10.
Li, J.: Developing machine learning and statistical methods for the analysis of genetics and genomics. UCLA Electronic Theses and Dissertations, pp. 119–146 (2021)
11.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR,abs/1409.1556 (2015)
12.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893, vol. 1 (2005)
13.
Wu, H., et al.: A deep learning, image based approach for automated diagnosis for inflammatory skin diseases. Ann. Transl. Med. 8(9), 581 (2020)
14.
Hu, J., Shen, L., Albanie, S., et al.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recogniton, pp. 7132–7141 (2018)
15.
Laitinen, E., Lohan, E.S., Talvitie, J., Shrestha, S.: Access point significance measures in WLAN-based location, pp. 24–29 (2012)
16.
Aijaz, S.F., Khan, S.J., Azim, F., Shakeel, C.S., Hassan, U.: Deep learning application for effective classification of different types of psoriasis. J. Healthcare Eng. 2022 (2022)
17.
Dwivedi, P., Khan, A.A., Gawade, A., Deolekar, S.: A deep learning based approach for automated skin disease detection using fast R-CNN. In: 2021 Sixth International Conference on Image Information Processing (ICIIP), vol. 6, pp. 116–120. IEEE (2021)
18.
Park, S., et al.: Model learning analysis of 3d optoacoustic Mesos copy images for the classification of atopic dermatitis. Biomed. Opt. Express 12(6), 3671–3683 (2021)CrossRef
19.
Jiang, Z., et al.: Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning. Sci. Rep. 12(1), 1–13 (2022)
20.
Khan, S., Rahmani, H., Shah, S.A.A., Bennamoun, M.: A guide to convolutional neural networks for computer vision. Synth. Lect. Comput. Vision 8(1), 1–207 (2018)CrossRef
21.
Kayalibay, B., Jensen, G., van der Smagt, P.: CNN-based segmentation of medical imaging data. arXiv preprint arXiv:​1701.​03056 (2017)
22.
Brownlee, J.: How to visualize filters and feature maps in convolutional neural networks. Machine Learning Mastery (2019)
Metadata
Title
Detection and Diagnosis of Atopic Dermatitis Using Deep Learning Network
Authors
Anh-Minh Nguyen
Van-Hieu Vu
Thanh-Binh Trinh
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Intelligent Systems and Networks

Print ISBN: 978-981-9947-24-9

Electronic ISBN: 978-981-9947-25-6

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-99-4725-6_19

Premium Partner

    Image Credits