Abstract
Melanoma is one of the most lethal forms of skin cancer caused when skin is exposed to intense UV rays. Estimates suggest that the deaths tolls are more than 50,000 with 3 million and more reports of it yearly. However, early diagnosis of malignant melanoma significantly curbs the mortality rate. Several computer-aided diagnosis systems have been proposed in assisting the detection of malignant melanoma in its earlier stages. These systems help in early detection and earlier diagnosis of many symptoms, which results in better and accurate treatment. However, the challenge starts from the first step of implementation of such systems, which is melanoma lesion detection in the image. In this paper, the problem of automatic detection of melanoma lesion on skin images is presented based on the concept of deep learning. The experiments have been performed using Convolutional Neural Networks (CNNs) with training input size of 15 × 15 and 50 × 50. The result of the study shows that deep learning using CNN is able to detect the melanoma lesion efficiently. The best performance has been achieved using CNN with 15 × 15 training input size. The performances obtained using this network is Jaccard index (0.90), Accuracy (95.85%), Precision (94.31%), Recall (94.31%), and F-value (94.14%) for the best performance.
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References
Siegel, R., Naishadham, D., Jemal, A.: Cancer statistics, 2013. CA Cancer J. Clin. 63(1), 11–30 (2013)
Garnavi, R., Aldeen, M., Bailey, J.: Computer-aided diagnosis of melanoma using border-and wavelet-based texture analysis. IEEE Trans. Inf. Technol. Biomed. 16(6), 1239–1252 (2012)
Garcia, C., Delakis, M.: Convolutional face finder: a neural architecture for fast and robust face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1408–1423 (2004)
Sabouri, P., Gholam Hosseini, H., Collins, J.: Border detection of skin lesions on a single system on chip. In: Advanced Technologies, Embedded and Multimedia for Human-centric Computing, pp. 465–471. Springer, Berlin (2014)
Toossi, M.T.B., Pourreza, H.R., Zare, H., Sigari, M.-H., Layegh, P., Azimi, A.: An effective hair removal algorithm for dermoscopy images. Skin Res. Technol. 19(3), 230–235 (2013)
U-Net: Convolutional Networks for Biomedical Image Segmentation, Olaf Ronneberger, Philipp Fischer, and Thomas Brox. ISBI cell tracking challenge (2015)
CNN-based Segmentation of Medical Imaging Data, Barıs¸ Kayalıbay Grady Jenseny Patrick van der Smagtz
Attia, M., Hosny, M., Nahavandi, S.: Skin melanoma segmentation using recurrent and convolutional neural networks. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
An atlas of clinical dermatology, Dec 2014. Available: http://www.danderm.dk/at
Interactive dermatology atlas, Dec 2014. Available: http://www.dermatlas.net/atlas/cfm
D. I. S. (DermIS), Dec 2013. Available: http://www.dermis.net
D. NZ, Dec 2014. Available: http://www.dermnetnz.org
“Dermquest,” Dec 2014. Available: https://www.dermquest.comLNCS Homepage, http://www.springer.com/lncs. Last accessed 21 Nov 2016
Ishida, T., Katsuragawa, S., Nakamura, K., Ashizawa, K., MacMahon, H., Doi, K.: Computerized analysis of interstitial lung diseases on chest radiographs based on lung texture, geometric-pattern features and artificial neural networks. Proc SPIE. 2002, vol. 4684, pp. 1331–1338. LNCS Homepage, http://www.springer.com/lncs. Last accessed 21 Nov 2016
Katsuragawa, S., Doi, K., MacMahon, H.: Image feature analysis and computer-aided diagnosis in digital radiography: classification of normal and abnormal lungs with interstitial disease in chest images. Med. Phys. 16, 38–44 (1989). [PubMed] LNCS Homepage, http://www.springer.com/lncs. Last accessed 21 Nov 2016
Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching
Karami, N., Esteki, A.: Automated diagnosis of melanoma based on nonlinear complexity features. In: 5th Kuala Lumpur International Conference on Biomedical Engineering 2011, pp. 270–274. Springer, Berlin (2011)
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Jadhav, A.R., Ghontale, A.G., Shrivastava, V.K. (2019). Segmentation and Border Detection of Melanoma Lesions Using Convolutional Neural Network and SVM. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_8
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DOI: https://doi.org/10.1007/978-981-13-1132-1_8
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