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Recent Deep Learning Methods for Melanoma Detection: A Review

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Mathematics and Computing (ICMC 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 834))

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Abstract

Melanoma is a type of skin cancer, which is not that common like basal cell and squamous carcinoma, but it has dangerous implications since it has the tendency to migrate to other parts of body. So, if it is detected at an early stage then we can easily treat; otherwise it becomes fatal. Many computer-aided diagnostic methods using dermoscopy images have been proposed to assist the clinicians and dermatologists. Along with conventional methods which extract the low level handcrafted features, nowadays researchers have focused towards deep learning techniques which extract the deep and more generic features. Since 2012, deep learning has been applied to classification, segmentation, localization and many other fields and made an impact. This paper reviews about the deep learning techniques to detect melanoma cases from the rest skin lesion in clinical and dermoscopy images.

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Authors and Affiliations

  1. School of Electrical Sciences, IIT Bhubaneswar, Bhubaneswar, 752050, India

    Nazneen N. Sultana & N. B. Puhan

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  1. Nazneen N. Sultana
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  2. N. B. Puhan
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Correspondence to Nazneen N. Sultana .

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Editors and Affiliations

  1. Department of Mathematical Sciences, Indian Institute of Technology BHU, Varanasi, Uttar Pradesh, India

    Debdas Ghosh

  2. Haldia Institute of Technology, Haldia, India

    Debasis Giri

  3. University of Central Florida, Orlando, Florida, USA

    Ram N. Mohapatra

  4. Istanbul Commerce University, Istanbul, Turkey

    Ekrem Savas

  5. Kyushu University, Fukuoka, Japan

    Kouichi Sakurai

  6. Indian Institute of Technology (BHU), Varanasi, India

    L. P. Singh

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Sultana, N.N., Puhan, N.B. (2018). Recent Deep Learning Methods for Melanoma Detection: A Review. In: Ghosh, D., Giri, D., Mohapatra, R., Savas, E., Sakurai, K., Singh, L. (eds) Mathematics and Computing. ICMC 2018. Communications in Computer and Information Science, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-0023-3_12

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  • DOI: https://doi.org/10.1007/978-981-13-0023-3_12

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