A Convolutional Deep Neural Network Approach for Skin Cancer Detection Using Skin Lesion Images
Authors: Firas Gerges, Frank Y. Shih
Abstract:
Malignant Melanoma, known simply as Melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient death. When detected early, Melanoma is curable. In this paper we propose a deep learning model (Convolutional Neural Networks) in order to automatically classify skin lesion images as Malignant or Benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a significant improvement over previous work, achieving an accuracy of 97%.
Keywords: Deep learning, skin cancer, image processing, melanoma.
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[1] “What is melanoma skin cancer?”
[Online]. Available: https://www.cancer.org/cancer/melanoma-skin-cancer/ about/what-is-melanoma.html
[2] M. MacGill, “What you should know about melanoma,” Medical News Today, 2018.
[Online]. Available: https://www.medicalnewstoday.com/articles/154322.php
[3] F. Nachbar, W. Stolz, T. Merkle, A. B. Cognetta, T. Vogt, M. Landthaler, P. Bilek, O. Braun-Falco, and G. Plewig, “The abcd rule of dermatoscopy: high prospective value in the diagnosis of doubtful melanocytic skin lesions,” Journal of the American Academy of Dermatology, vol. 30, no. 4, pp. 551–559, 1994.
[4] G. Argenziano, G. Fabbrocini, P. Carli, V. De Giorgi, E. Sammarco, and M. Delfino, “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, vol. 134, no. 12, pp. 1563–1570, 1998.
[5] P. Dubal, S. Bhatt, C. Joglekar, and S. Patii, “Skin cancer detection and classification,” in 2017 6th international conference on electrical engineering and informatics (ICEEI). IEEE, Conference Proceedings, pp. 1–6.
[6] R. Moussa, F. Gerges, C. Salem, R. Akiki, O. Falou, and D. Azar, “Computer-aided detection of melanoma using geometric features,” in 2016 3rd Middle East Conference on Biomedical Engineering (MECBME). IEEE, Conference Proceedings, pp. 125–128.
[7] S. Jain and N. Pise, “Computer aided melanoma skin cancer detection using image processing,” Procedia Computer Science, vol. 48, pp. 735–740, 2015.
[8] A. Bhardwaj and J. Bhatia, “An image segmentation method for early detection and analysis of melanoma,” IOSR Journal of Dental and Medical Sciences, vol. 13, no. 10, pp. 18–22., 2014.
[9] N. F. M. Azmi, H. M. Sarkan, Y. Yahya, and S. Chuprat, “Abcd rules segmentation on malignant tumor and benign skin lesion images,” in 2016 3rd International Conference on Computer and Information Sciences (ICCOINS). IEEE, Conference Proceedings, pp. 66–70.
[10] P. G. Cavalcanti, J. Scharcanski, and G. V. Baranoski, “A two-stage approach for discriminating melanocytic skin lesions using standard cameras,” Expert Systems with Applications, vol. 40, no. 10, pp. 4054–4064, 2013.
[11] X. Yuan, Z. Yang, G. Zouridakis, and N. Mullani, “Svm-based texture classification and application to early melanoma detection,” in 2006 International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, Conference Proceedings, pp. 4775–4778.
[12] J. L. G. Arroyo and B. G. Zapirain, “Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis,” Computers in biology and medicine, vol. 44, pp. 144–157, 2014.
[13] C. Salem, D. Azar, and S. Tokajian, “An image processing and genetic algorithm-based approach for the detection of melanoma in patients,” Methods of information in medicine, vol. 57, no. 01/02, pp. 74–80, 2018.
[14] A. Agarwal, A. Issac, M. K. Dutta, K. Riha, and V. Uher, “Automated skin lesion segmentation using k-means clustering from digital dermoscopic images,” in 2017 40th International Conference on Telecommunications and Signal Processing (TSP). IEEE, Conference Proceedings, pp. 743–748.
[15] R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in Proceedings of the 25th international conference on Machine learning. ACM, Conference Proceedings, pp. 160–167.
[16] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, Conference Proceedings, pp. 770–778.
[17] G. Hinton, L. Deng, D. Yu, G. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition,” IEEE Signal processing magazine, vol. 29, 2012.
[18] E. Nasr-Esfahani, S. Samavi, N. Karimi, S. M. R. Soroushmehr, M. H. Jafari, K. Ward, and K. Najarian, “Melanoma detection by analysis of clinical images using convolutional neural network,” in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Conference Proceedings, pp. 1373–1376.
[19] E. Zagrouba and W. Barhoumi, “A prelimary approach for the automated recognition of malignant melanoma,” Image Analysis and Stereology, vol. 23, no. 2, pp. 121–135, 2011.
[20] C. Munteanu and S. Cooclea, “Spotmole—melanoma control system,” 2009.
[21] I. Giotis, N. Molders, S. Land, M. Biehl, M. F. Jonkman, and N. Petkov, “Med-node: a computer-assisted melanoma diagnosis system using non-dermoscopic images,” Expert systems with applications, vol. 42, no. 19, pp. 6578–6585, 2015.
[22] L. Yu, H. Chen, Q. Dou, J. Qin, and P.-A. Heng, “Automated melanoma recognition in dermoscopy images via very deep residual networks,” IEEE transactions on medical imaging, vol. 36, no. 4, pp. 994–1004, 2016.
[23] A. Menegola, M. Fornaciali, R. Pires, F. V. Bittencourt, S. Avila, and E. Valle, “Knowledge transfer for melanoma screening with deep learning,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE, Conference Proceedings, pp. 297–300.
[24] L. Torrey and J. Shavlik, Transfer learning. IGI Global, 2010, pp. 242–264.
[25] J. Kawahara, A. BenTaieb, and G. Hamarneh, “Deep features to classify skin lesions,” in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEE, Conference Proceedings, pp. 1397–1400.
[26] N. Codella, J. Cai, M. Abedini, R. Garnavi, A. Halpern, and J. R. Smith, “Deep learning, sparse coding, and svm for melanoma recognition in dermoscopy images,” in International Workshop on Machine Learning in Medical Imaging. Springer, Conference Proceedings, pp. 118–126.
[27] M. H. Jafari, N. Karimi, E. Nasr-Esfahani, S. Samavi, S. M. R. Soroushmehr, K. Ward, and K. Najarian, “Skin lesion segmentation in clinical images using deep learning,” in 2016 23rd International conference on pattern recognition (ICPR). IEEE, Conference Proceedings, pp. 337–342.
[28] Y. Yuan, M. Chao, and Y.-C. Lo, “Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance,” IEEE transactions on medical imaging, vol. 36, no. 9, pp. 1876–1886, 2017.
[29] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016.