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Detection and Classification of Malignant Melanoma Using Deep Features of NASNet

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Abstract

Skin cancer is one of the leading causes of death worldwide. Malignant melanoma is the most lethal form of skin cancer which can spread to other areas of the body. Although it is rare, still 75% of skin cancer deaths are caused due to malignant melanoma. Early detection can help to combat melanoma. However, early detection is very challenging due to various visual similarities between melanoma and non-melanoma. In this paper, we present an automated system for early melanoma detection. Our technique is based on deep transfer learning, in which we utilized a pre-trained neural network model named as NASNet. The features from the pre-trained model are transferred to the new dataset to detect melanoma. We modified the original architecture and added global average pooling and our classification layers. To overcome issues of a smaller dataset, we use carefully selected label and feature-preserving geometric transformations to increase images. The proposed model is trained on dermoscopic images from the International Skin Imaging Collaboration (ISIC 2020) dataset. As compared to the prior methods, our proposed model is efficient and shows state-of-the-art performance with an accuracy of more than 97% on the test dataset.

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Authors and Affiliations

  1. Department of Computer Science, University of Engineering and Technology (UET), Lahore, Pakistan

    Qaiser Abbas

  2. Department of Information Technology, Lahore Leads University, Lahore, Pakistan

    Anza Gul

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  1. Qaiser Abbas
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  2. Anza Gul
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Correspondence to Qaiser Abbas.

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This article is part of the topical collection “Innovative AI in Medical Applications” guest edited by Lydia Bouzar-Benlabiod, Stuart H. Rubin and Edwige Pissaloux.

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Abbas, Q., Gul, A. Detection and Classification of Malignant Melanoma Using Deep Features of NASNet. SN COMPUT. SCI. 4, 21 (2023). https://doi.org/10.1007/s42979-022-01439-9

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