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A Case for Exploration: Exploratory Data Analysis in Neural Networks for Renal Tumor Classification

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Proceedings of the ICR’22 International Conference on Innovations in Computing Research (ICR 2022)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1431))

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Abstract

Exploratory data analysis provides insights into the structure and representation of data. By understanding data, more representative networks can be created without total reliance on implicit processes to match internal representations with expectation. The aim of this paper is to demonstrate how the usage of exploratory data analysis combined with knowledge from subject matter experts can increase the performance of networks and reduce reliance on mechanics. We use manual transformations based on data analysis to represent our data in a way that resembles the abstract processes that experts use to classify renal tumors. Compared to a baseline network, we achieved a significant increase in accuracy, 93.3% to 98.64%, without loss in external validations, while reducing the network size considerably. The reliance on internal mechanics to achieve the expected representation is flawed and exploratory data analysis can mitigate some of the pitfalls of this approach, leading to increased performance and a reduction in required compute power.

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Author information

Authors and Affiliations

  1. IMT, Roskilde University, Roskilde, Denmark

    Mikkel Pedersen & Henrik Bulskov

Authors
  1. Mikkel Pedersen
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  2. Henrik Bulskov
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Corresponding author

Correspondence to Mikkel Pedersen .

Editor information

Editors and Affiliations

  1. University of Detroit Mercy, Detroit, MI, USA

    Kevin Daimi

  2. Kent Institute Australia, Sydney, NSW, Australia

    Abeer Al Sadoon

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Pedersen, M., Bulskov, H. (2022). A Case for Exploration: Exploratory Data Analysis in Neural Networks for Renal Tumor Classification. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the ICR’22 International Conference on Innovations in Computing Research. ICR 2022. Advances in Intelligent Systems and Computing, vol 1431. Springer, Cham. https://doi.org/10.1007/978-3-031-14054-9_15

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