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
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Multimedia Systems
Erschienen in: Multimedia Systems 4/2022

30.08.2021 | Special Issue Paper

Automatic segmentation of melanoma skin cancer using transfer learning and fine-tuning

verfasst von: Rafael Luz Araújo, Flávio H. D. de Araújo, Romuere R. V. e Silva

Erschienen in: Multimedia Systems | Ausgabe 4/2022

Einloggen

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

search-config
loading …

Abstract

The massive use of multimedia technologies has enabled the exploration of information in many data such as texts, audio, videos, and images. Computational methods are being developed for several purposes such as monitoring, security, business, and even health through the automatic diagnosis of diseases by medical images. Among these diseases, we have melanoma skin cancer. Melanoma is a skin cancer that causes a large number of fatalities worldwide. Several methods for the automatic diagnosis of melanoma in dermoscopic images have been developed. For these methods to be more efficient, it is essential to isolate the lesion region. This study used a melanoma segmentation method based on U-net and LinkNet deep learning networks combined with transfer learning and fine-tuning techniques. Additionally, we evaluate the model’s ability to learn to segment the disease or just the dataset by combining datasets. The experiments were carried out in three datasets (PH2, ISIC 2018, and DermIS) and obtained promising results, emphasizing the U-net that obtained an average Dice of 0.923 in the PH2 dataset, Dice =  0.893 in ISIC 2018, and Dice = 0.879 in the DermIS dataset.
Vorheriger Artikel Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system
Nächster Artikel Applying deep learning-based multi-modal for detection of coronavirus

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
Fußnoten
1
The codes, databases, and information for using the proposed methodology are available in a public repository: https://​github.​com/​rafaluz/​skin-seg-unet-linknet.
 
Literatur
1.
Memon, M.H., Khan, A., Li, J.-P., Shaikh, R.A., Memon, I., Deep, S.: Content based image retrieval based on geo-location driven image tagging on the social web, in: 2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing (ICCWAMTIP), IEEE, 2014, pp. 280–283
2.
Memon, I.: Authentication user’s privacy: An integrating location privacy protection algorithm for secure moving objects in location based services. Wireless Personal Communications 82(3), 1585–1600 (2015)
3.
Memon, M.H., Li, J.-P., Memon, I., Arain, Q.A.: Geo matching regions: multiple regions of interests using content based image retrieval based on relative locations. Multimedia Tools Appl. 76(14), 15377–15411 (2017)CrossRef
4.
Said, Y., Atri, M.: Efficient and high-performance pedestrian detector implementation for intelligent vehicles. IET Intell. Trans. Syst. 10(6), 438–444 (2016)CrossRef
5.
Arain, Q.A., Memon, H., Memon, I., Memon, M.H., Shaikh, R.A., Mangi, F.A.: Intelligent travel information platform based on location base services to predict user travel behavior from user-generated gps traces. Int. J. Comput. Appl. 39(3), 155–168 (2017)
6.
Amin, J., Sharif, A., Gul, N., Anjum, M.A., Nisar, M.W., Azam, F., Bukhari, S.A.C.: Integrated design of deep features fusion for localization and classification of skin cancer. Pattern Recognit. Lett. 131, 63–70 (2020)CrossRef
7.
Al Nazi, Z., Abir, T.A.: Automatic skin lesion segmentation and melanoma detection: Transfer learning approach with u-net and dcnn-svm, in: Proceedings of International Joint Conference on Computational Intelligence, Springer, 2020, pp. 371–381
8.
9.
Haenssle, H.A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., Kalloo, A., Hassen, A.B.H., Thomas, L., Enk, A., et al.: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann. Oncol. 29(8), 1836–1842 (2018)CrossRef
10.
Argenziano, G., Soyer, H.P.: Dermoscopy of pigmented skin lesions-a valuable tool for early. Lancet Oncol. 2(7), 443–449 (2001)CrossRef
11.
Moura, N., Veras, R., Aires, K., Machado, V., Silva, R., Araújo, F., Claro, M.: Abcd rule and pre-trained cnns for melanoma diagnosis. Multimedia Tools Appl. 78(6), 6869–6888 (2019)CrossRef
12.
Barata, C., Ruela, M., Francisco, M., Mendonça, T., Marques, J.S.: Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst. J. 8(3), 965–979 (2013)CrossRef
13.
Tang, P., Liang, Q., Yan, X., Xiang, S., Sun, W., Zhang, D., Coppola, G.: Efficient skin lesion segmentation using separable-unet with stochastic weight averaging. Comput. Methods Programs Biomed. 178, 289–301 (2019)CrossRef
14.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer, 2015, pp. 234–241
15.
Chaurasia, A., Culurciello, E., Linknet: Exploiting encoder representations for efficient semantic segmentation, in, : IEEE Visual Communications and Image Processing (VCIP). IEEE 2017, 1–4 (2017)
16.
Hosny, K.M., Kassem, M.A., Foaud, M.M.: Classification of skin lesions using transfer learning and augmentation with alex-net. PloS one 14(5), e0217293 (2019)
17.
18.
19.
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybernet. 9(1), 62–66 (1979)CrossRef
20.
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations, in: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 1, Oakland, CA, USA, 1967, pp. 281–297
21.
Fan, H., Xie, F., Li, Y., Jiang, Z., Liu, J.: Automatic segmentation of dermoscopy images using saliency combined with otsu threshold. Comput. Biol. Med. 85, 75–85 (2017)CrossRef
22.
Ahn, E., Kim, J., Bi, L., Kumar, A., Li, C., Fulham, M., Feng, D.D.: Saliency-based lesion segmentation via background detection in dermoscopic images. IEEE J. Biomed. Health Informatics 21(6), 1685–1693 (2017)CrossRef
23.
Al-Masni, M.A., Al-Antari, M.A., Choi, M.-T., Han, S.-M., Kim, T.-S.: Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Comput. Methods Programs Biomed. 162, 221–231 (2018)CrossRef
24.
Aljanabi, M., Özok, Y.E., Rahebi, J., Abdullah, A.S.: Skin lesion segmentation method for dermoscopy images using artificial bee colony algorithm. Symmetry 10(8), 347 (2018)CrossRef
25.
Peng, Y., Wang, N., Wang, Y., Wang, M.: Segmentation of dermoscopy image using adversarial networks. Multimedia Tools Appl. 78(8), 10965–10981 (2019)CrossRef
26.
Goyal, M., Oakley, A., Bansal, P., Dancey, D., Yap, M.H.: Skin lesion segmentation in dermoscopic images with ensemble deep learning methods. IEEE Access 8, 4171–4181 (2019)CrossRef
27.
Khan, M.Q., Hussain, A., Rehman, S.U., Khan, U., Maqsood, M., Mehmood, K., Khan, M.A.: Classification of melanoma and nevus in digital images for diagnosis of skin cancer. IEEE Access 7, 90132–90144 (2019)CrossRef
28.
Abraham, N., Khan, N.M.: A novel focal tversky loss function with improved attention u-net for lesion segmentation, in, : IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE 2019, 683–687 (2019)
29.
Santos, E., Veras, R., Miguel, H., Aires, K., Claro, M.L., Junior, G.B.: A skin lesion semi-supervised segmentation method, in: 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), IEEE, 2020, pp. 33–38
30.
Guo, X., Chen, Z., Yuan, Y.: Complementary network with adaptive receptive fields for melanoma segmentation, in, : IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE 2020, 2010–2013 (2020)
31.
Araújo, R.L., Ricardo de Andrade, L.R., Rodrigues, J.J., e Silva, R.R.: Automatic segmentation of melanoma skin cancer using deep learning, in: 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM), IEEE, 2021, pp. 1–6
32.
Al-Masni, M.A., Al-Antari, M.A., Park, J.-M., Gi, G., Kim, T.-Y., Rivera, P., Valarezo, E., Choi, M.-T., Han, S.-M., Kim, T.-S.: Simultaneous detection and classification of breast masses in digital mammograms via a deep learning yolo-based cad system. Comput. Methods Programs Biomed. 157, 85–94 (2018)CrossRef
33.
Mendonca, T., Celebi, M., Mendonca, T., Marques, J.: Ph2: A public database for the analysis of dermoscopic images, in: Dermoscopy image analysis, CRC Press, 2015
34.
Codella, N., Rotemberg, V., Tschandl, P., Celebi, M.E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., et al.: Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic), arXiv preprint arXiv:​1902.​03368
35.
Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018)CrossRef
36.
37.
Bi, L., Kim, J., Ahn, E., Kumar, A., Feng, D., Fulham, M.: Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern Recognit. 85, 78–89 (2019)CrossRef
38.
39.
Zeng, C., Zhu, Z., Liu, G., Hu, W., Wang, X., Yang, C., Wang, H., He, D., Tan, J.: Randomized, double-blind, placebo-controlled trial of oral enalapril in patients with neurally mediated syncope. Am. Heart J. 136(5), 852–858 (1998)CrossRef
40.
Provost, F., Domingos, P.: Well-trained pets: Improving probability estimation trees, Raport instytutowy IS-00-04, Stern School of Business, New York University
41.
Ginsberg, J.R., Young, T.P.: Measuring association between individuals or groups in behavioural studies. Animal Behaviour 44(1), 377–379 (1992)CrossRef
42.
Hamers, L., et al.: Similarity measures in scientometric research: The jaccard index versus salton’s cosine formula. Information Processing and Management 25(3), 315–18 (1989)
43.
Burman, P.: A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika 76(3), 503–514 (1989)MathSciNetCrossRef
44.
Chakkaravarthy, A.P., Chandrasekar, A.: Automatic detection and segmentation of melanoma using fuzzy c-means, in: 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Vol. 1, IEEE, 2019, pp. 132–136
45.
Dey, N., Rajinikanth, V., Ashour, A.S., Tavares, J.M.R.: Social group optimization supported segmentation and evaluation of skin melanoma images. Symmetry 10(2), 51 (2018)CrossRef
46.
Rastrelli, M., Tropea, S., Rossi, C.R., Alaibac, M.: Melanoma: epidemiology, risk factors, pathogenesis, diagnosis and classification. vivo 28(6), 1005–1011 (2014)
Metadaten
Titel
Automatic segmentation of melanoma skin cancer using transfer learning and fine-tuning
verfasst von
Rafael Luz Araújo
Flávio H. D. de Araújo
Romuere R. V. e Silva
Publikationsdatum
30.08.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 4/2022
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-021-00840-3

Weitere Artikel der Ausgabe 4/2022

Multimedia Systems 4/2022 Zur Ausgabe

Special Issue Paper

Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends

Special Issue Paper

A novel method for ECG signal classification via one-dimensional convolutional neural network

Special Issue Paper

Intelligent multimodal medical image fusion with deep guided filtering

Special Issue Paper

AI inspired EEG-based spatial feature selection method using multivariate empirical mode decomposition for emotion classification

Special Issue Paper

Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

Regular Paper

Predicting skeleton trajectories using a Skeleton-Transformer for video anomaly detection

Neuer Inhalt

    Bildnachweise