Algorithmic Compression Learning: A Hybrid Complexity–Deep Representation Framework for Medical Image Analysis
DOI:
https://doi.org/10.15837/ijccc.2026.5.7478Keywords:
Algorithmic Complexity, Compression-Based Features, Hybrid Deep Learning, Structural Pattern AnalysisAbstract
Understanding and quantifying structural complexity in visual data remains a fundamental challenge in pattern recognition. This work investigates the hypothesis that compression-based measures can provide a complementary structural representation that enhances deep learning models. To this end, we introduce a hybrid framework termed Algorithmic Compression Learning with Deep Learning (ACL–DL), which integrates multi-scale compression-based descriptors with deep neural representations. The proposed ACL component characterizes structural irregularity and stability across spatial scales using compression-derived measures, interpreted as empirical proxies of structural organization. These descriptors are fused with semantic embeddings extracted from fine-tuned deep networks, enabling joint modeling of intrinsic structural patterns and highlevel visual features. Experiments conducted on three heterogeneous medical imaging datasets (BUSI ultrasound, CBIS-DDSM mammography, and BreakHis histopathology) demonstrate that compression-based descriptors provide discriminative signals that complement deep representations. The best accuracy rates achieved by the ACL–DL hybrid framework, reported as mean + standard deviation, reached 95.63%, 98.72%, and 99.16% across the three datasets, consistently improving over standalone ACL and deep learning baselines with statistically significant gains (p < 0.05). Beyond predictive performance, the results highlight the potential of compression-based structural descriptors as model-agnostic components that can be effectively integrated into modern representation learning pipelines.
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