Automatic detection of melanoma using broad extraction of features from digital images

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© 2016 IEEE. Automatic and reliable diagnosis of skin cancer, as a smartphone application, is of great interest. Among different types of skin cancers, melanoma is the most dangerous one which causes most deaths. Meanwhile, melanoma is curable if it were diagnosed in its early stages. In this paper we propose an efficient system for prescreening of pigmented skin lesions for malignancy using general-purpose digital cameras. These images can be captured by a smartphone or a digital camera. This could be beneficial in different applications, such as computer aided diagnosis and telemedicine applications. It could assist dermatologists, or smartphone users, evaluate risk of suspicious moles. The proposed method enhances borders and extracts a broad set of dermatologically important features. These discriminative features allow classification of lesions into two groups of melanoma and benign. This method is computationally appropriate as a smartphone application. Experimental results show that our proposed method is superior in diagnosis accuracy compared to state-of-the-art methods.




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. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (Vol. 2016-October, pp. 1357–1360). Institute of Electrical and Electronics Engineers Inc.

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