Identification of Melanoma Using Convolutional Neural Networks for Non Dermoscopic Images

R. Rangarajan, V. Sesha Gopal, R. Rengasri, J. Premaladha () and K. S. Ravichandran ()
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R. Rangarajan: SASTRA Deemed-to-be-University, School of Computing
V. Sesha Gopal: SASTRA Deemed-to-be-University, School of Computing
R. Rengasri: SASTRA Deemed-to-be-University, School of Computing
J. Premaladha: SASTRA Deemed-to-be-University, School of Computing
K. S. Ravichandran: SASTRA Deemed-to-be-University, School of Computing

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 839-846 from Springer

Abstract: Abstract In recent times, Melanoma has become one of the most dreadful type of skin cancers with mortality rates being high. Although there exist state of the art methods for identification of melanoma, the usefulness of automated approach such as deep learning proves to be very much appealing. This paper deals with Convolutional neural network framework, which has been evaluated for non-dermoscopic images of melanoma and benign nevi for early diagnosis and efficient classification. The image dataset has 70 images of melanoma and 100 images of benign nevi, which was augmented to 1020 images and then split into two groups. These groups are trained, and a two-fold validation is done for achieving better accuracy.

Keywords: Melanoma; Deep learning; Convolutional Neural Networks; Training; Validation (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_84

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DOI: 10.1007/978-3-030-41862-5_84

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