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Using KullBack-Liebler Divergence Based Meta-learning Algorithm for Few-Shot Skin Cancer Image Classification: Literature Review and a Conceptual Framework

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Advances in Computing and Data Sciences (ICACDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1614))

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Abstract

Meta-learning often termed “learning to learn,” seeks to construct models that can swiftly learn new information or adapt to new situations using only a few training data samples. Meta-learning is distinct from traditional supervised learning. The model is required to recognise training data before generalising to unknown test data in traditional supervised learning. Meta-learning, on the other hand, has only one goal: to learn. Few-shot makes predictions based on a limited amount of data using a prototypical network that it learns using a metric space in which classification is done by computing distances between prototype representations of each class. The Kullback-Leibler Divergence (KLD) a non-symmetric distance measure between two probability distributions, p(x) and q(x) is used in the meta-learning process of few-shot leaning to compute the distance between the prototype class to query cancerous image. This study seeks to investigate the usefulness of KLD in cancer image classification to assist medical practitioner in early diagnosis of the dreadful disease.

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Acknowledgments

Special thanks and appreciation go out to my supervisors, Prof. C. Du and Prof. S. Ajila, for their professional guidance during this research study, as well as my colleague Olusola Salami, who assisted in the review of this paper and provided advice on the flow that was used.

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Authors and Affiliations

  1. Computer System Engineering Department, Tshwane University of Technology, Pretoria, South Africa

    Olusoji B. Akinrinade & Chunglin Du

  2. System and Computer Engineering Department, Carleton University, Ottawa, Canada

    Samuel Ajila

Authors
  1. Olusoji B. Akinrinade
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  2. Chunglin Du
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  3. Samuel Ajila
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Corresponding author

Correspondence to Olusoji B. Akinrinade .

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Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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Akinrinade, O.B., Du, C., Ajila, S. (2022). Using KullBack-Liebler Divergence Based Meta-learning Algorithm for Few-Shot Skin Cancer Image Classification: Literature Review and a Conceptual Framework. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1614. Springer, Cham. https://doi.org/10.1007/978-3-031-12641-3_9

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  • DOI: https://doi.org/10.1007/978-3-031-12641-3_9

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