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An Ensemble Method for Efficient Classification of Skin Lesion from Dermoscopy Image

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Computer Vision and Image Processing (CVIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1376))

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Abstract

Nowadays, skin cancer is growing-up due to exposure to Ultraviolet (UV) radiation emanating from the sun light. Among several categories of skin lesion, melanoma is the most deadly cancerous kind. Diagnosing skin lesion in its early stage have a great chance to cure the disease. Researchers have proposed several computer-aided diagnosis techniques to detect skin lesions. In this work, we present an ensemble model to classify skin lesion using a pre-trained DenseNet and InceptionV3 algorithms. The fully layered fine-tuned technique is applied to both the algorithms which are previously explored for ImageNet dataset. The fine-tuned algorithms are utilized to train on the HAM10000 dataset. The classification results obtained due to the pre-trained models are concatenated in the average ensemble method. The experimentation on the standard datasets confirm the classification accuracy of 91% and indicates that the proposed approach is a promising as compared to the previously developed approaches.

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Authors and Affiliations

  1. Mangalore University, Mangalagangothri, Karnataka, India

    B. H. Shekar & Habtu Hailu

Authors
  1. B. H. Shekar
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  2. Habtu Hailu
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Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology Allahabad, Prayagraj, India

    Satish Kumar Singh

  2. Indian Institute of Technology Roorkee, Roorkee, India

    Partha Roy

  3. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  4. Indian Institute of Information Technology Allahabad, Prayagraj, India

    P. Nagabhushan

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Shekar, B.H., Hailu, H. (2021). An Ensemble Method for Efficient Classification of Skin Lesion from Dermoscopy Image. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1376. Springer, Singapore. https://doi.org/10.1007/978-981-16-1086-8_15

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  • DOI: https://doi.org/10.1007/978-981-16-1086-8_15

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  • Online ISBN: 978-981-16-1086-8

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