Diagnosis of malignant melanoma by neural network ensemble-based system utilising hand-crafted skin lesion features

• Autorzy: Grochowski Michał , Mikołajczyk Agnieszka , Kwasigroch Arkadiusz

Identyfikatory

DOI

10.24425/mms.2019.126327

• Języki publikacji: EN

Abstrakty

EN

Malignant melanomas are the most deadly type of skin cancer, yet detected early have high chances of successful treatment. In the last twenty years, the interest in automatic recognition and classification of melanoma dynamically increased, partly because of appearing public datasets with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task due to uneven sizes of datasets, huge intra-class variation with small interclass variation, and the existence of many artifacts in the images. One of the most recognized methods of melanoma diagnosis is the ABCD method. In the paper, we propose an extended version of this method and an intelligent decision support system based on neural networks that uses its results in the form of hand-crafted features. Automatic determination of the skin features with the ABCD method is difficult due to the large diversity of images of various quality, the existence of hair, different markers and other obstacles. Therefore, it was necessary to apply advanced methods of pre-processing the images. The proposed system is an ensemble of ten neural networks working in parallel, and one network using their results to generate a final decision. This system structure enables to increase the efficiency of its operation by several percentage points compared with asingle neural network. The proposed system is trained on over 5000 and tested afterwards on 200 skin moles. The presented system can be used as a decision support system for primary care physicians, as a system capable of self-examination of the skin with a dermatoscope and also as an important tool to improve biopsy decision making.

Słowa kluczowe

EN

decision support; diagnostics; image processing; artificial neural networks; ensemble of neural networks; melanoma malignant

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2019
• Tom: Vol. 26, nr 1
• Strony: 65--80
• Opis fizyczny: Bibliogr. 31 poz., rys., tab., wykr.

Twórcy

autor

Grochowski Michał

• Gdańsk University of Technology, Faculty of Electrical and Control Engineering, G. Narutowicza 11/12,80-222, Gdańsk, Poland

autor

Mikołajczyk Agnieszka

• Gdańsk University of Technology, Faculty of Electrical and Control Engineering, G. Narutowicza 11/12,80-222, Gdańsk, Poland

autor

Kwasigroch Arkadiusz

• Gdańsk University of Technology, Faculty of Electrical and Control Engineering, G. Narutowicza 11/12,80-222, Gdańsk, Poland

Bibliografia

• [1] Nachbar, F., Stolz, W., Merkle, T., Cognetta, A.B., Vogt, T., Landthaler, M., et al. (1994). The ABCD rule of dermatoscopy: high prospective value in the diagnosis of doubtful melanocytic skin lesions. J. Am. Acad. Dermatol., 30, 551-559.
• [2] Johr, R.H. (2002). Dermoscopy alternative melanocytic algorithms - the ABCD rule of dermatoscopy, menzies scoring method, and 7-point checklist. Clin. Dermatol., 20, 240-247.
• [3] Henning, J.S., Dusza, S.W., Wang, S.Q., Marghoob, A.A., Rabinovitz, H.S., Polsky, D., et al. (2017).The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy. J. Am. Acad. Dermatol., 56, 45-52.
• [4] MoleMap, https://molemap.net.au. (Jan. 2018).
• [5] ISIC Archive, https://isic-archive.com. (Jan. 2018).
• [6] ISBI 2017, http://biomedicalimaging.org/2017/challenges. (Jan. 2018).
• [7] Su, J., Vargas, D.V., Kouichi, S. (2017). One pixel attack for fooling deep neural networks. ArXiv171008864 Cs Stat.
• [8] Gopinathan, S., Rani, S.N.A. (2016). The melanoma skin cancer detection and feature extraction through image processing techniques. Orthopedics, 5, 11.
• [9] Jafari, M.H., Samavi, S., Karimi, N., Soroushmehr, S.M.R., Ward, K., Najarian, K. (2016). Automatic detection of melanoma using broad extraction of features from digital images. Eng. Med. Biol. Soc. EMBC 2016 IEEE 38th Annu. Int. Conf. On, IEEE, 1357-1360.
• [10] Gutman, D., Codella, N.C., Celebi, E., Helba, B., Marchetti, M., Mishra, N., et al. (2016). Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). ArXiv PreprArXiv160501397
• [11] Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., et al. (2017). Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). ArXiv PreprArXiv171005006.
• [12] Jamil, U., Khalid, S., Akram, M.U. (2016). Dermoscopic feature analysis for melanoma recognition and prevention. Innov. Comput. Technol. INTECH 2016 Sixth Int. Conf. On, IEEE, 290-295.
• [13] Ge, Z., Demyanov, S., Bozorgtabar, B., Abedini, M., Chakravorty, R., Bowling, A., et al. (2017). Exploiting local and generic features for accurate skin lesions classification using clinical and dermoscopy imaging. Biomed. Imaging ISBI 2017 2017 IEEE 14th Int. Symp. On, IEEE, 986-990.
• [14] Mikołajczyk, A., Kwasigroch, A., Grochowski, M. (2017). Intelligent system supporting diagnosis of malignant melanoma. In Polish Control Conference Springer, Cham., 828-837.
• [15] Fiorese, M, Peserico, E, Silletti, A. (2011). VirtualShave: automated hair removal from digital dermatoscopic images. Eng. Med. Biol. Soc. EMBC 2011 Annu. Int. Conf. IEEE, 5145-5148.
• [16] Toossi, M.T.B., Pourreza, H.R., Zare, H., Sigari, M.H., Layegh, P., Azimi, A. (2013). An effective hair removal algorithm for dermoscopy images. Skin Res. Technol., 19, 230-235.
• [17] Harris, C., Stephens, M. (1988). A combined corner and edge detector. Alvey Vis. Conf., 15, Manchester, UK, 10-5244.
• [18] Erkaymaz, O., Ozer, M., Perc, M. (2017). Performance of small-world feedforward neural networks for the diagnosis of diabetes. Applied Mathematics and Computation, 15, 311, 22-28.
• [19] Grochowski, M., Wąsowicz, M., Mikołajczyk, A., Ficek, M., Kulka, M., Wróbel, M., Jędrzejewska-Szczerska, M. (2019). Machine Learning System For Automated Blood Smear Analysis. Metrol. Meas. Syst., 26(1).
• [20] Qian, G., Zhang, L. (2018). A simple feedforward convolutional conceptor neural network for classification. Applied Soft Computing, 1, 70, 1034-41.
• [21] Frery, A.C., Rangayyan, R.M., Azevedo-Marques, P.M., Ramos, H.S. (2018). Evaluation of Deep Feedforward Neural Networks for Classification of Diffuse Lung Diseases. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. 22nd Iberoamerican Congress, CIARP2017, Valparaíso, Chile, 10657, 152, Springer.
• [22] Mirchandani, M.S., Pendse, M., Rane, P., Vedula, A. (2018). Plant disease detection and classification using image processing and artificial neural networks. Plant Disease, 5(06).
• [23] Bebis, G., Georgiopoulos, M. (1994). Feed-forward neural networks. IEEE Potentials, 13(4), 27–31.
• [24] Zhou, Z.H., Wu, J., Tang, W. (2002). Ensembling neural networks: many could be better than all. Artificial intelligence, 1, 137(1-2), 239-263.
• [25] Williams, P.M. (1995). Bayesian regularization and pruning using a Laplace prior. Neural computation, 7(1), 117-143.
• [26] Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. InIjcai, 14(2), 1137-1145.
• [27] Zhu, W., Zeng, N., Wang, N. (2010). Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. NESUG proceedings: health care and life sciences, Baltimore, 14, 19, 67.
• [28] Kwasigroch, A., Mikołajczyk, A., Grochowski, M. (2017). Deep neural networks approach to skin lesions classification - A comparative analysis. Methods Models Autom. Robot. MMAR 2017 22nd Int. Conf. On, IEEE, 1069-1074.
• [29] Kwasigroch, A., Mikołajczyk, A., Grochowski, M. (2017). Deep convolutional neural networks as a decision support tool in medical problems - malignant melanoma case study. Pol. Control Conf., Springer, 848-856.
• [30] Yu, Z., Jiang, X., Zhou, F., Qin, J., Ni, D., Chen, S., Lei, B., Wang, T. (2018). Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features. IEEE Transactions on Biomedical Engineering.
• [31] Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115.

Uwagi

EN

1. This work was supported by the Ministry of Science and Higher Education, Poland (Diamond Grant #DI2016020746).

PL

2. Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).