Abstract
Melanoma is a serious form of skin cancer that develops from pigment-producing cells known as melanocytes, which in turn produce melanin that gives your skin its color. Early detection of these symptoms will certainly help affected people to overcome their suffering and find appropriate solutions for their treatment methods. That is why researchers have tried in many studies to provide technical solutions to help early detection of skin cancer. In this paper, a smart pre-trained model based on deep learning techniques for the early detection of Melanoma and Nevus has been proposed. It is designed to track and divide the dynamic features of the dermoscopic ISIC dataset into two distinguished classes Melanoma and Nevus of epidermal pathologies. AlexNet and GoogLeNet are used to classify each cancer type according to their profile features. It was found that the average classification accuracy for the above-mentioned algorithms is 90.2% and 89% respectively, providing plausible results when comparing to other existing models.
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References
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)
Silveira, M., Nascimento, J.C., Marques, J.S., Marcal, A.R.S., Mendonca, T., Yamauchi, S., Maeda, J., Rozeira, J.: Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE J. Sel. Top. Sign. Process. 3(1), 35–45 (2009)
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 Inf. 21(6), 1685–1693 (2017)
Codella, N.C.F., Nguyen, Q.-B., Pankanti, S., Gutman, D.A., Helba, B., Halpern, A.C., Smith, J.R.: Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J. Res. Dev. 61(4/5), 5:1−5:15 (2017)
Li, Y., Shen, L.J.S.: Skin lesion analysis towards melanoma detection using deep learning network. Sensors 18(2), 556 (2018)
Adjed, F., Gardezi, S.J.S., Ababsa, F., Faye, I., Dass, S.C.: Fusion of structural and textural features for melanoma recognition. IET Comput. Vis. 12(2), 185–195 (2017)
Mukherjee, S., Adhikari, A., Roy, M.: Malignant melanoma classification using cross-platform dataset with deep learning CNN architecture. In: Bhattacharyya, S., Pal, S.K., Pan, I., Das, A. (eds.) Recent Trends in Signal and Image Processing: Proceedings of ISSIP 2018, pp. 31–41. Springer, Singapore (2019)
Mahbod, A., Schaefer, G., Wang, C., Ecker, R., Ellinge, I.: Skin lesion classification using hybrid deep neural networks. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1229–1233. IEEE (2019)
Qaisar Abbas, M., Celebi, E.: DermoDeep-A classification of melanoma-nevus skin lesions using multi-feature fusion of visual features and deep neural network. Multimedia Tools Appl. 78(16), 23559–23580 (2019)
Pathan, S., Prabhu, K.G., Siddalingaswamy, P.C.: Control: techniques and algorithms for computer aided diagnosis of pigmented skin lesions—a review. Biomed. Sig. Process. Control 39, 237–262 (2018)
Stolz, W.: ABCD rule of dermatoscopy: a new practical method for early recognition of malignant melanoma. Eur. J. Dermatol 4, 521–527 (1994)
Menzies, S.W., Ingvar, C., Crotty, K.A., McCarthy, W.H.: Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features. Arch. Dermatol. 132(10), 1178–1182 (1996)
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. Arch. Dermatol. 134(12), 1563–1570 (1998)
Henning, J.S., Dusza, S.W., Wang, S.Q., Marghoob, A.A., Rabinovitz, H.S., Polsky, D., Kopf, A.W.: The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy. J. Am. Acad. Dermatol. 56(1), 45–52 (2007)
Mitchell, T.C., Karakousis, G., Schuchter, L.: Melanoma. In: Abeloff's Clinical Oncology. pp. 1034–1051. e1032. Elsevier (2020)
What is Melanoma Skin Cancer ? https://www.cancer.org/cancer/melanoma-skin-cancer/about/what-is-melanoma.html (2019). Accessed 16 May 2020
Massi, G., LeBoit, P.E.: Common nevus. In: Massi, G., LeBoit, P.E. (eds.) Histological Diagnosis of Nevi and Melanoma, pp. 29–46. Springer, Berlin (2014)
Massi, G., LeBoit, P.E.: Histological Diagnosis of Nevi and Melanoma. Springer, Berlin (2013)
Kittler, H., Pehamberger, H., Wolff, K., Binder, M.J.T.l.O.: Diagnostic accuracy of dermoscopy. Lancet Oncol. 3(3), 159–165 (2002)
ISIC Dataset. https://challenge2019.isic-archive.com/ (2019). Accessed 1 May 2020
Society, A.C.: Cancer Facts & Figures 2019. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2019/cancer-facts-and-figures-2019.pdf (2019). Accessed 30 May 2019
P. Tschandl, C.R., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. arXiv:1710.05006.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105 (2012)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. arXiv 2014. 1409 (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.J.: Going deeper with convolutions. CoRR. (2014)
Lopez, A.R., Giro-i-Nieto, X., Burdick, J., Marques, O.: Skin lesion classification from dermoscopic images using deep learning techniques. In: 2017 13th IASTED international conference on biomedical engineering (BioMed), pp. 49–54. IEEE (2017)
Gutman, D., Codella, N.C., Celebi, E., Helba, B., Marchetti, M., Mishra, N., Halpern, A.J.: 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) (2016)
Prathiba, M., Jose, D., Saranya, R.: Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks. In: IOP Conference Series: Materials Science and Engineering 2019, vol. 1, p. 012107. IOP Publishing
Matsunaga, K., Hamada, A., Minagawa, A., Koga, H.: Image classification of melanoma, nevus and seborrheic keratosis by deep neural network ensemble (2017)
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), 994–1004 (2016)
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Sallam, A., Ba Alawi, A.E., Saeed, A.Y.A. (2021). A CNN-Based Model for Early Melanoma Detection. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_5
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DOI: https://doi.org/10.1007/978-3-030-70713-2_5
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