WSEAS Transactions on Business and Economics
Print ISSN: 1109-9526, E-ISSN: 2224-2899
Volume 23, 2026
Toward Neuro-Symbolic and Reservoir-Inspired Medical Imaging: A
BD-CeNN Autoencoder with ASP Rule Mining for Robust and
Explainable Interpretation of Grayscale and Color Images
Authors: , , , , ,
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Abstract: We present a neuro-symbolic framework for medical image analysis that integrates a Binary Discrete
Cellular Neural Network (BD-CeNN) autoencoder, a reservoir-computing–inspired BD-CeNN refinement stack,
and automatically mined Answer Set Programming (ASP) rules. The autoencoder converts grayscale and color
inputs (CT, MRI, histopathology, dermatology) into discrete, symbolic latent codes, which are iteratively refined
to improve robustness and diagnostic discrimination. From annotated cases, ASP rules capture human-readable
relations and constraints, enabling transparent, auditable reasoning over the learned symbols while maintaining
predictive performance. The hybrid design targets resource-constrained clinical environments where trust,
explainability, and adaptability are essential. This paper details the conceptual architecture, motivation, and
deployment feasibility; extensive benchmarking is left for future work, laying the groundwork for accessible,
interpretable AI in medical imaging.
Keywords:
Neuro-symbolic artificial intelligence, explainable medical imaging, Binary Discrete Cellular Neural
Networks (BD-CeNN), reservoir computing, Answer Set Programming (ASP), multimodal
symbolic encoding, symbolic feature extraction, temporal symbolic reasoning, interpretable clinical
decision support.
Pages: 304-324
DOI: 10.37394/23207.2026.23.23