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A boosting-based transfer learning method to address absolute-rarity in skin lesion datasets and prevent weight-drift for melanoma detection

Lokesh Singh (Department of Information Technology, National Institute of Technology, Raipur, India)
Rekh Ram Janghel (Department of Information Technology, National Institute of Technology, Raipur, India)
Satya Prakash Sahu (Department of Information Technology, National Institute of Technology, Raipur, India)

Data Technologies and Applications

ISSN: 2514-9288

Article publication date: 20 June 2022

Issue publication date: 17 March 2023

98

Abstract

Purpose

Automated skin lesion analysis plays a vital role in early detection. Having relatively small-sized imbalanced skin lesion datasets impedes learning and dominates research in automated skin lesion analysis. The unavailability of adequate data poses difficulty in developing classification methods due to the skewed class distribution.

Design/methodology/approach

Boosting-based transfer learning (TL) paradigms like Transfer AdaBoost algorithm can compensate for such a lack of samples by taking advantage of auxiliary data. However, in such methods, beneficial source instances representing the target have a fast and stochastic weight convergence, which results in “weight-drift” that negates transfer. In this paper, a framework is designed utilizing the “Rare-Transfer” (RT), a boosting-based TL algorithm, that prevents “weight-drift” and simultaneously addresses absolute-rarity in skin lesion datasets. RT prevents the weights of source samples from quick convergence. It addresses absolute-rarity using an instance transfer approach incorporating the best-fit set of auxiliary examples, which improves balanced error minimization. It compensates for class unbalance and scarcity of training samples in absolute-rarity simultaneously for inducing balanced error optimization.

Findings

Promising results are obtained utilizing the RT compared with state-of-the-art techniques on absolute-rare skin lesion datasets with an accuracy of 92.5%. Wilcoxon signed-rank test examines significant differences amid the proposed RT algorithm and conventional algorithms used in the experiment.

Originality/value

Experimentation is performed on absolute-rare four skin lesion datasets, and the effectiveness of RT is assessed based on accuracy, sensitivity, specificity and area under curve. The performance is compared with an existing ensemble and boosting-based TL methods.

Keywords

Acknowledgements

Ethics approval: This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent: Informed consent was obtained from all individual participants included in the study.

Funding: No funding is provided for experimentation.

Conflict of interest: The authors declare that there is no conflict of interest regarding the publication of this paper.

Citation

Singh, L., Janghel, R.R. and Sahu, S.P. (2023), "A boosting-based transfer learning method to address absolute-rarity in skin lesion datasets and prevent weight-drift for melanoma detection", Data Technologies and Applications, Vol. 57 No. 1, pp. 1-17. https://doi.org/10.1108/DTA-10-2021-0296

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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