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May 22, 2026Machine Learning and Knowledge ExtractionOpen Access

Explainable Artificial Intelligence (XAI) for Cancer Classification in Medical Imaging: A Systematic Review

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Authors

KGKhairil Imran GhauthYKYanche Ari Kustiawan

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Overview

Systematic review examines XAI techniques enhancing transparency in cancer imaging, suggesting future research directions.

Key Points

  • This study aims to analyze the role of Explainable Artificial Intelligence (XAI) in improving cancer detection through medical imaging.
  • Conducted a systematic literature review following PRISMA guidelines with 926 records identified from major databases.
  • Screened 46 studies meeting inclusion criteria based on quality assessment.
  • Analyzed XAI techniques, model architectures, interpretability mechanisms, and evaluation practices.
  • Gradient-based methods, especially Grad-CAM, are found to be dominant due to their integration with convolutional neural networks.
  • Evaluation methods are heterogeneous, relying mostly on qualitative visual inspections.
  • Challenges include instability in explanations, coarse localization, high computational costs, and limitations with transformer models.

Cite This Study

Ghauth et al. (2026) studied this question.

synapsesocial.com/papers/6a0ff33bd674f7c03778bbc6https://doi.org/10.3390/make8050134
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