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A Study on an Ensemble Model for Automatic Classification of Melanoma from Dermoscopy Images

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Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1776))

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Abstract

Accurate melanoma classification from dermoscopy images is challenging due to low contrasts between skin lesions and normal tissue regions. The intraclass variance of melanomas in terms of color, texture, shape, size, uncertain boundary, and location of lesions in dermoscopy images adds to the complexity. Artifacts like body hair, blood vessels, nerves, ruler, and ink marks in dermoscopy images also hinder the classification performance. In this work, an inpainting technique has been studied to handle artifacts. Transfer learning models, namely EfficientNet B4, B5, DenseNet121, and Inception-ResNet V2, have been studied. A study on ensembling the results of all the mentioned models is also performed. The robustness of the models has been tested using stratified K-fold cross-validation with test time augmentation (TTA). The trained models with the mentioned inpainting technique outperformed several deep learning solutions in the literature on the SIIM-ISIC Melanoma Classification Challenge dataset. EfficientNet B5 has achieved the best AUC of 0.9287 among the stand-alone models, and the ensemble solution has achieved an AUC of 0.9297.

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Notes

  1. 1.

    SIIM-ISIC Melanoma Classification Challenge Live Leaderboard.

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Authors and Affiliations

  1. Indian Institute of Information Technology Design and Manufacturing, Kancheepuram, Chennai, 600127, India

    Anil Kumar Adepu, Subin Sahayam & Umarani Jayaraman

  2. Apollo Institute of Medical Sciences and Research, Hyderabad, 500090, India

    Rashmika Arramraju

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  1. Anil Kumar Adepu
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  2. Subin Sahayam
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  3. Rashmika Arramraju
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  4. Umarani Jayaraman
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Correspondence to Subin Sahayam .

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Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

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Adepu, A.K., Sahayam, S., Arramraju, R., Jayaraman, U. (2023). A Study on an Ensemble Model for Automatic Classification of Melanoma from Dermoscopy Images. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1776. Springer, Cham. https://doi.org/10.1007/978-3-031-31407-0_48

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  • DOI: https://doi.org/10.1007/978-3-031-31407-0_48

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