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An overview of classification methods from dermoscopy images in skin lesion diagnostic

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Przegląd metod klasyfikacji obrazów dermatoskopowych wykorzystywanych w diagnostyce zmian skórnych
Języki publikacji
EN
Abstrakty
EN
The article contains a review of selected classification methods of dermatoscopic images with human skin lesions, taking into account various stages of dermatological disease. The described algorithms are widely used in the diagnosis of skin lesions, such as artificial neural networks (CNN, DCNN), random forests, SVM, kNN classifier, AdaBoost MC and their modifications. The effectiveness, specificity and accuracy of classifications based on the same data sets were also compared and analyzed.
PL
Artykuł zawiera przegląd wybranych metod klasyfikacji obrazów dermatoskopowych zmian skórnych człowieka z uwzględnieniem różnych etapów choroby dermatologicznej. Opisane algorytmy są szeroko wykorzystywane w diagnostyce zmian skórnych, takie jak sztuczne sieci neuronowe (CNN, DCNN), random forests, SVM, klasyfikator kNN, AdaBoost MC i ich modyfikacje. Porównana i przeanalizowana została również skuteczność, specyficznośc i dokładność klasyfikatów w oparciu o te same zestawy danych.
Rocznik
Strony
36--39
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
  • Lublin University of Technology, Department of Electronics and Information Technology, Lublin, Poland
autor
  • Danylo Halytsky Lviv National Medical University, Department of Medical Informatics, Lviv, Ukraine
Bibliografia
  • [1] Abbas Q., Celebi M.E., Serrano C., Fondo´n Garcı´ I., Maa G.: Pattern classification of dermoscopy images: A perceptually uniform model. Pattern Recognition 46, 2013, 86–97.
  • [2] Abedini M., Chen Q., Codella N.C.F., Garnavi R., Sun X.: Accurate and scalable system for automatic detection of malignant melanoma. Dermoscopy Image Analysis. CRC Press, Boca Raton 2015.
  • [3] Alendar F., Kittler H, Helppikangas H., Alendar T.: Clear definitions,simple terminology, no metaphoric terms. Expert Rev. Dermatol. 3, 2008, 27–29.
  • [4] Argenziano G., Fabbrocini G., Carli P., De Giorgi V., Sammarco E., Delfino M.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions, comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Archives of Dermatology 134, 1998, 1563– 1570.
  • [5] Argenziano G., Soyer H.P., Chimenti S., Talamini R., Corona R., Sera F.: Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. Journal of American Academy of Dermatology 48(5), 2003, 679–693.
  • [6] Barata, C., Ruela, M.: Two Systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Systems Journal 8(3), 2014, 965–979.
  • [7] Blum H., Ellwanger U.: Digital image analysis for diagnosis of cutaneous melanoma, development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions. British Journal of Dermatology 151(5), 2004, 1029–1038.
  • [8] Blum H., Luedtke H., Ellwanger U., Schwabe R., Rassner G., Garbe C.: Digital image analysis for diagnosis of cutaneous melanoma, development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions. Computerized Medical Imaging and Graphics 31(6), 2007, 362–373.
  • [9] Burroni M., Corona R., Dell’Eva G., Sera F.: Melanoma computer -aided diagnosis reliability and feasibility study. Clinical Cancer Research 10(6), 2004, 1881–1886.
  • [10] Celebi M.E., Aslandogan Y.A., Stoecker W.V., Iyatomi H., Oka H., Chen X.: Unsupervised border detection in dermoscopy images. Skin Res Technol. 13, 2007, 1–9.
  • [11] Celebi M. E., Kingravi H.A., Uddin B., Iyatomi H., Aslandogan Y.A., Stoecker W.V., Moss R.H.: A methodological approach to the classification of dermoscopy images. Computerized Medical Imaging and Graphics 31(6), 2007, 362–373.
  • [12] Celebi M.E., Kingravia H.A., Uddina B., Iyatomid H., Aslandogana Y.A., Stoeckerb W.V., Mossc R.H.: A methodological approach to the classification of dermoscopy images. Comput Med Imaging Graph. 31(6), 2007, 362–373.
  • [13] Codella N., Cai J., Abedini M., Garnavi R., Halpern A., Smith J. R.: Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images, Machine Learning in Medical Imaging. Springer, Munich 2015.
  • [14] Ercal F., Chawla A., Stoecker W.V., Lee H.-C., Moss R.H.: Neural network diagnosis of malignant melanoma from color images. IEEE Transactions on Biomedical Engineering 41(9), 1994, 837–845.
  • [15] Esteva, A.: Dermatologist-level classification of skin cancer with deep neural networks. Nat. Res. 542(7639), 2017, 115–118.
  • [16] Esteva A., Kuprel B., Novo R.A., Ko J., Swetter S. M., Bla H.M, Thrun S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 2017, 115–118.
  • [17] Ge Z., Demyanov S., Chakravorty R., Bowling A., Garnavi R.: Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images. Springer Cham LNCS 10435, 2017, 250–258.
  • [18] Green A., Martin N., McKenzie G., Pfitzner J., Quintarelli F., Thomas B. W., O'Rourke M., Knight N.: Computer image analysis of pigmented skin lesions, Melanoma research 1(4), 1991, 231–236.
  • [19] Gutman D., Codella N., Celebi E., Helba B., Marchetti M., Mishra N., Halpern A.: Skin lesion analysis toward melanoma detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, International Skin Imaging Collaboration (ISIC). eprint arXiv:1605.01397.
  • [20] Husemann R., Tölg S., Seelen W.V., Altmeyer P., Frosch P.J., Stücker M., Hoffmann K., El-Gammal S.: Computerised diagnosis of skin cancer using neural networks. Skin Cancer and UV Radiation. Springer, Berlin 1997.
  • [21] Kahofer P., Hofmann-Wellenhof R., Smolle J.: Tissue counter analysis of dermatoscopic images of melanocytic skin tumours: preliminary findings. Melanoma research 12(1), 2002, 71–75.
  • [22] Kittler H., Riedl E., Rosendahl C., Cameron A.: Dermatoscopy of unpigmented lesions of the skin: A new classification of vessel morphology based on pattern analysis. Dermatopathology: Practical & Conceptual 14(4), 2018, 3.
  • [23] Kruk M., Świderski B., Osowski S., Kurek J., Słowińska M., Walecka I.: Melanoma recognition using extended set of descriptors and classifiers. J Image Video Proc. 43, 2015 [https://doi.org/10.1186/s13640-015-0099-9].
  • [24] Menzies S.W., Bischof L, Talbot H, Gutenev A, Avramidis M, Wong L.: The performance of SolarScan: An automated dermoscopy image analysis instrument for the diagnosis of primary melanoma. Arch Dermatol. 141(11), 2005, 1388–1396.
  • [25] Menzies S., Ingvar C., Crotty K., McCarthy W.: Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features. Archives of Dermatology 132, 1996, 1178–1182.
  • [26] Michalska M.: Klasyfikacja zmian skórnych z obrazów dermatoskopwych, Wybrane zagadnienia z zakresu elektrotechniki, inżynierii biomedycznej i budownictwa. Prace doktorantów Politechniki Lubelskiej 2019, 108–120.
  • [27] Piątkowska W., Martyna J., Nowak L.: A decision support system based on the semantic analysis of melanoma images using multi-elitist PSO and SVM. Proceedings of the 7th International Conference on Machine Learning and Data Mining in Pattern Recognition MLDM’11 1, 2011, 362–374.
  • [28] Romero-Lopez A., Giro-i-Nieto X., Burdick J., Marques O.: Skin lesion classification from dermoscopic images using deep learning techniques. Proc. of Biomedical Engineering 2017 [https://doi.org/10.2316/P.2017.852-053].
  • [29] Rosendahl C., Cameron A., McColl I., Wilkinson I.: Dermatoscopy in routine practice. Chaos and Clues. Australian Family Physician 41(7), 2012.
  • [30] Schaefer G., Krawczyk B., Celebi M.E., Iyatomi H.: An ensemble classification approach for melanoma diagnosis, Memetic Computing 6(4), 2014, 233–240.
  • [31] Shahid M., Khan S.: Dermoscopy images classification based on color, texture and shape features using SVM. The 3rd International Conference on Next Generation Computing (INC GC2017b), 243–245.
  • [32] Xie F., Fan H., Li Y., Jiang Z., Meng R., Bovik A.: Melanoma classification on dermoscopy images using a neural network ensemble model. IEEE Transationa on Medical Imaging 36(3), 2017, 849–858.
  • [33] Yu L., Chen H., Dou,Q., Qin J., Heng P.A.: Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans. Med. Imaging 36(4), 2017, 994–1004.
  • [34] Zhang J., Xie Y., Wu Q., Xia Y.: Skin lesion classification in dermoscopy images using synergic deep learning. Springer Nature Switzerland, LNCS 11071, 2018, 12–20.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-66bc15b2-31ec-4a03-97e9-424baa6165f0
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