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Selecting Textural Characteristics of Chest X-Rays for Pneumonia Lesions Classification with the Integrated Gradients XAI Attribution Method

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Explainable Artificial Intelligence (xAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1901))

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Abstract

Global texture characteristics are powerful tools for solving medical image classification tasks. There are many such characteristics like Grey-Level Co-occurrence Matrices, Grey-Level Run-Length Matrices, Grey-Level Size Zone Matrices, texture matrices and others. However, not all are important when solving particular image classification tasks, while their calculation requires many computational resources. The current work aims to evaluate the importance of each characteristic, taking into account a large dimensionality of the texture characteristics matrices. To achieve this aim, it is proposed to use neural networks and a novel mean integrated gradient eXplainable Artificial Intelligence method to achieve the stated aim. The experiment showed that texture matrices with higher mean integrated gradient values are more important than others while solving pneumonia lesions classification tasks on X-Ray lung images. The result also indicates that classification quality does not degrade and even improves after shrinking the feature set with the proposed method. These facts prove that the mean integrated gradients can be used for solving feature selection tasks for classification purposes.

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Acknowledgements

We want to thank Giuliano Anselmi from IBM for granting us access to computing resources and helping us configure the IBM power stations our models were trained on.

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Authors and Affiliations

  1. Artificial Intelligence and Cognitive Load Research Lab, School of Computer Science, Technological University Dublin, Dublin, Ireland

    Oleksandr Davydko & Luca Longo

  2. The National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine

    Vladimir Pavlov

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  1. Oleksandr Davydko
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  2. Vladimir Pavlov
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  3. Luca Longo
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Correspondence to Oleksandr Davydko .

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  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

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Davydko, O., Pavlov, V., Longo, L. (2023). Selecting Textural Characteristics of Chest X-Rays for Pneumonia Lesions Classification with the Integrated Gradients XAI Attribution Method. In: Longo, L. (eds) Explainable Artificial Intelligence. xAI 2023. Communications in Computer and Information Science, vol 1901. Springer, Cham. https://doi.org/10.1007/978-3-031-44064-9_36

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  • DOI: https://doi.org/10.1007/978-3-031-44064-9_36

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  • Online ISBN: 978-3-031-44064-9

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