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Selected applications of deepneural networks in skin lesion diagnostic

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Warianty tytułu
PL
Wybrane zastosowania głębokich sieci neuronowych w diagnozie zmian skórnych
Języki publikacji
EN
Abstrakty
EN
The article provides an overview of selected applications of deep neural networks in the diagnosis of skin lesions from human dermatoscopic images, including many dermatological diseases, including very dangerous malignant melanoma. The lesion segmentation process, features selectionand classification was described.Application examples of binary and multiclass classification are given.The described algorithms have been widely used in the diagnosis of skin lesions. The effectiveness, specificity, and accuracy of classifiers were compared and analyzed based on available datasets.
PL
Artykuł zawiera przeglądwybranychzastosowań głębokich sieci neuronowych w diagnostyce zmian skórnych zobrazów dermatoskopowych człowieka z uwzględnieniem wielu choróbdermatologicznych, w tym bardzo niebezpiecznejz nich malignant melanoma. Został opisany processegmentacjizmiany, selekcji cech i klasyfikacji. Uwzględniono przykłady binarnej i wieloklasowej klasyfikacji. Opisane algorytmy znalazły szerokie zastosowanie w diagnostyce zmian skórnych.Porównano i przeanalizowanoskuteczność, specyficznośći dokładność klasyfikatorów w oparciu o dostępne zestawy danych.
Rocznik
Strony
18--21
Opis fizyczny
Bibliogr. 38 poz., tab., rys., wykr.
Twórcy
  • Lublin University of Technology, Department of Electronics and Information Technology, Lublin, Poland
Bibliografia
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  • [12] Ge Y. et al.: Melanoma segmentation and classification in clinical images using deep learning. Proceedings of the 2018 10th International Conference on Machine Learning and Computing, 2018, 252–256.
  • [13] Ge Z. et al.: Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images. Springer, Cham LNCS 10435, 2017, 250–258.
  • [14] Haenssle H. 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 29/2018, 1836–1342.
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  • [16] He K., Zhang X, Ren, S., Sun J.: Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 770–778.
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  • [18] Kittler H. et al.: Dermatoscopy of unpigmented lesions of the skin: A new classification of vessel morphology based on pattern analysis. Dermatopathology: Practical & Conceptual 14(4), 2008, 3.
  • [19] Li Y., Shen L.: Skin lesion analysis towards melanoma detection using deep learning network. Sensors 18, 2018, 556.
  • [20] Lopez A. R. et al.: Skin lesion classification from dermatoscopic images using deep learning techniques. Biomedical Engineering 852, 2017, 852-053 [http://doi.org/10.2316/P.2017.852-053].
  • [21] Maia L. et al.: Evaluation of melanoma diagnosis using deep features. 25th International Conference on Systems, Signals and Image Processing (IWSSIP), 2018, 1–4.
  • [22] Marchetti M. A. et al.: Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge: comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. J Am Acad Dermatol 78, 2018, 270–277.
  • [23] Marchetti M. A. et al: Computer algorithms show potential for improving dermatologists’ accuracy to diagnose cutaneous melanoma: results of the international skin imaging collaboration 2017. J Am Acad Dermatol 82, 2020, 622–627.
  • [24] Maron R. C. et al.: Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. Eur J Cancer 119, 2019, 57–65.
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  • [26] Murphree D. H. et al.: Deep learning for dermatologists: Part I. J Am Acad Dermatol 2020, 1–9, [http://doi.org/10.1016/j.jaad.2020.05.05].
  • [27] Nida N. et al.: Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. International Journal of Medical Informatics 124, 2019, 37–48.
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  • [29] Phillips M. et al.: Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions. JAMA Netw Open 2, 2019, 1913436.
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  • [34] Wang Y. et al.: Incorporating clinical knowledge with constrained classifier chain into a multimodal deep network for melanoma detection. Computers in Biology and Medicine 137, 2021, 104812.
  • [35] Young A. T. et al.: Artificial intelligence in dermatology: A Primer. Journal of Investigative Dermatology 140, 2020, 1504–1512.
  • [36] Yu L. et al.: Automated melano-ma recognition in dermoscopy images via very deep residual networks. IEEE transactions on medical imaging 36(4), 2017, 994–1004.
  • [37] Zhang J. et al.: Skin lesion classification in dermoscopy images using synergic deep learning. Springer Nature Switzerland, LNCS 11071, 2018, 12–20.
  • [38] Zhang X.: Melanoma segmentation based on deep learning. Computer Assisted Surgery 22, 2017, 267–277.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b140b97f-5af8-4d6d-8765-bd9b8a023c8f
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