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Deep neural networks for skin lesions diagnostics

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Warianty tytułu
PL
Głębokie sieci neuronowe dla diagnostyki zmian skórnych
Języki publikacji
EN
Abstrakty
EN
Non-invasive diagnosis of skin cancer is extremely necessary. In recent years, deep neural networks and transfer learning have been very popular in the diagnosis of skin diseases. The article contains selected basics of deep neural networks, their interesting applications created in recent years, allowing the classification of skin lesions from available dermatoscopic images.
PL
Nieinwazyjna diagnostyka nowotworów skóry jest niezwykle potrzebna. W ostatnich latach bardzo dużym zainteresowaniem w diagnostyce chorób skóry cieszą się głębokie sieci neuronowe i transfer learning. Artykuł zawiera wybrane podstawy głębokich sieci neuronowych, ich ciekawe zastosowania stworzone w ostatnich latach, pozwalające na klasyfikację zmian skórnych z dostępnych obrazów dermatoskopowych.
Rocznik
Strony
50--53
Opis fizyczny
Bibliogr. 39 poz., rys., tab., wykr.
Twórcy
  • Lublin University of Technology, Department of Electronics and Information Technology, Lublin, Poland
Bibliografia
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  • [2] Al-Masni M. A., Kim D. H., Kim T. S:. Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Comput Methods Programs Biomed. 190, 105351, 2020 [http://doi.org/10.1016/j.cmpb.2020.105351].
  • [3] Brinker T. J. et al: Deep learning outperformed 136 of 157 dermatologists in a head-to-head der moscopic melanoma image classification task. Eur J Cancer 113, 47–54, 2019.
  • [4] Chaturvedi S. S., Gupta K., Prasad P. S.: Skin lesion analyser: An efficient seven-way multi-class skin cancer classification using MobileNet. Advances in Intelligent Systems and Computing 1141, Springer, Singapore, 2020 [http://doi.org/10.1007/978-981-15-3383-9_15].
  • [5] Codella N. C. F. et al.: Deep learning ensembles for melanoma recognition in dermoscopy images. IBM Journal of Research and Development 61(4/5), 173, 2017.
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  • [7] Gavrilov D., Lazarenko L., Zakirov E.: AI recognition in skin pathologies detection. Proceedings of the 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI), 554–542, Belgrade 2019.
  • [8] Ge Y. et al.: Melanoma segmentation and classification in clinical images using deep learning. 10th International Conference on Machine Learning and Computing ICMLC, 2018, 252–256.
  • [9] Gessert N. et al.: Skin lesion classification using CNNs with patch-based attention and diagnosis-guided loss weighting. IEEE Trans. Biomed. Eng. 67, 495–503, 2020.
  • [10] Haenssle H. A. et al: Man against machine reloaded: performance of a marketapproved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermato-logists working under less artificial conditions. Ann Oncol 31, 137–143, 2020.
  • [11] Harangi B.: Skin lesion classification with ensembles of deep convolutional neural networks. J. Biomed. Inform. 86, 25–32, 2018 [http://doi.org/10.1016/j.jbi.2018.08.006].
  • [12] Hasan M. M., Elahi M., Alam M. A.: DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning and augmentation. medRxiv 2021.02.02.21251038 [http://doi.org/10.1101/2021.02.02.21251038].
  • [13] He K. et al.: Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016, 770–778.
  • [14] Hekler A. et al.: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images, Eur J Cancer 118, 91–96, 2019.
  • [15] Howard A. G. et al.: MobileNets: Efficient convolutional neural networks for mobile vision applications. Computer Science, Computer Vision and Pattern Recognition, arXiv:1704.04861v1 [http://doi.org/10.48550/arXiv.1704.04861].
  • [16] Huang G. et al.: Densely Connected Convolutional Networks. Computer Vision and Pattern Recognition arXiv:1608.06993v5 [http://doi.org/10.48550/arXiv.1608.06993].
  • [17] Iqbal I. et al.: Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images. Computerized Medical Imaging and Graphics 88, 101843, 2021 [http://doi.org/10.1016/j.compmedimag.2020.101843].
  • [18] ISIC Archive [https://www.isic-archive.com/#!/topWithHeader/onlyHeaderTop/gallery] (accessed 23.03.2022).
  • [19] Kareem O., Mohsin Abdulazeez A., Zeebaree D.: Skin Lesions Classification Using Deep Learning Techniques: Review. Asian Journal of Research in Computer Science 9(1), 1–22, 2021 [http://doi.org/10.9734/AJRCOS/2021/v9i130210].
  • [20] Lee S. et al.: Augmented decision-making for acrallentiginous melanoma detection using deep convolutional neural networks. J. Eur. Acad. Dermatol. Venereol. 34, 1842–1850, 2020.
  • [21] Lopez A. R. et al.: Skin lesion classification from dermatoscopic images using deep learning techniques. 13th International Conference on Biomedical Engineering (BioMed) IASTED, 2017, 49–54 [http://doi.org/10.2316/P.2017.852-053].
  • [22] Maglogiannis I., Doukas C. N.: Overview of advanced computer vision systems for skin lesions characterization, IEEE transactions on information technology in biomedicine 13(5), 721–733, 2009.
  • [23] Mahbod A. et al.: Fusing finetuned deep features for skin lesion classification, Comput. Med. Imaging Graph. 71, 19–29, 2019 [http://doi.org/10.1016/j.compmedimag.2018.10.007].
  • [24] Mahdianpari M. et al.: Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sens. 10(7), 2018.
  • [25] 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, 622–627, 2020.
  • [26] Maron R. C. et al.: Systematic outperformance of 112 dermato-logists in multiclass skin cancer image classification by convolutional neural networks, Eur J Cancer 119, 57–65, 2019.
  • [27] MED-NODE Dataset [http://www.cs.rug.nl/~imaging/databases/melanoma_naevi/] (accessed 23.03.2022).
  • [28] 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, 37–48, 2019.
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  • [31] Qin Z. et al.: A GAN-based image synthesis method for skin lesion classification. Computer Methods and Programs in Biomedicine, 105568, 2020.
  • [32] Raza R. et al.: Melanoma Classification from dermoscopy images using ensemble of convolutional neural networks. Mathematics 10, 26, 2022 [http://doi.org/10.3390/math10010026].
  • [33] Sandler M. et al.: MobileNetV2: Inverted Residuals and Linear Bottlenecks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, 4510–4520.
  • [34] Simonyan K., Zisserman A.: Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations ICLR, 2015.
  • [35] Szegedy C. et al.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. AAAI, 4278–4284, 2017 [http://doi.org/arXiv:1602.07261].
  • [36] Tschandl P. et al.: Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, webbased, international, diagnostic study. Lancet Oncol 2019b(20), 938–947, 2019.
  • [37] Villa-Pulgarin J. et al.: Optimized convolutional neural network models for skin lesion classification, Computers, Materials & Continua Tech Science Press, CMC 70(2), 2022
  • [38] Yu C. et al.: Acral melanoma detection using a convolutional neural network for dermoscopy images. PLoS ONE 2018, 13, e0193321, 2018.
  • [39] Zakład Epidemiologii i Prewencji Nowotworów Centrum Onkologii – Instytut w Warszawie. Krajowy Rejestr Nowotworów (KRN) [http://onkologia.org.pl/] (accessed 02.08.2019).
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ddff9f32-bfbe-4676-8352-21e638159e05
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