Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
MRI Brain Tumor Segmentation Using Bidimensional Empirical Mode Decomposition and Morphological Operations Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

Detection and Diagnosis of Atopic Dermatitis Using Deep Learning Network

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

Erschienen in: Intelligent Systems and Networks

Verlag: Springer Nature Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Vorheriges Kapitel Power Management for Distributed DC Microgrid Under Minimum System Price
Nächstes Kapitel Prediction of the Welding Process Parameters and the Weld Bead Geometry for Robotic Welding Applications with Adaptive Neuro-Fuzzy Models
Literatur
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)
Metadaten
Titel
Detection and Diagnosis of Atopic Dermatitis Using Deep Learning Network
verfasst von
Anh-Minh Nguyen
Van-Hieu Vu
Thanh-Binh Trinh
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
Buch
Intelligent Systems and Networks

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

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

Copyright-Jahr: 2023

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

Premium Partner

    Bildnachweise