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A CNN-Based Model for Early Melanoma Detection

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Innovative Systems for Intelligent Health Informatics (IRICT 2020)

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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Correspondence to Amer Sallam .

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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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