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Algorithmic Compression Learning: A Hybrid Complexity–Deep Representation Framework for Medical Image Analysis

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Τίτλος: Algorithmic Compression Learning: A Hybrid Complexity–Deep Representation Framework for Medical Image Analysis
Συγγραφείς: Mjahed, Soukaina, Mjahed, Ouail
Πηγή: INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL; Vol. 21 No. 5 (2026): International Journal of Computers Communications & Control (October) ; 1841-9844 ; 1841-9836 ; 10.15837/ijccc.2026.5
Στοιχεία εκδότη: Agora University Press
Έτος έκδοσης: 2026
Συλλογή: Agora University Editing House: Journals
Θεματικοί όροι: Algorithmic Complexity, Compression-Based Features, Hybrid Deep Learning, Structural Pattern Analysis
Περιγραφή: 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.
Τύπος εγγράφου: article in journal/newspaper
Περιγραφή αρχείου: application/pdf
Γλώσσα: English
Relation: https://univagora.ro/jour/index.php/ijccc/article/view/7478/2616; https://univagora.ro/jour/index.php/ijccc/article/view/7478
DOI: 10.15837/ijccc.2026.5.7478
Διαθεσιμότητα: https://univagora.ro/jour/index.php/ijccc/article/view/7478
https://doi.org/10.15837/ijccc.2026.5.7478
Rights: Copyright (c) 2026 Soukaina MJAHED, Ouail MJAHED ; http://creativecommons.org/licenses/by-nc/4.0
Αριθμός Καταχώρησης: edsbas.5AC59BA0
Βάση Δεδομένων: BASE
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  Data: Algorithmic Compression Learning: A Hybrid Complexity–Deep Representation Framework for Medical Image Analysis
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  Data: INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS &amp; CONTROL; Vol. 21 No. 5 (2026): International Journal of Computers Communications &amp; Control (October) ; 1841-9844 ; 1841-9836 ; 10.15837/ijccc.2026.5
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  Data: 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 &lt; 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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  Data: Copyright (c) 2026 Soukaina MJAHED, Ouail MJAHED ; http://creativecommons.org/licenses/by-nc/4.0
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      – SubjectFull: Compression-Based Features
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      – SubjectFull: Hybrid Deep Learning
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      – SubjectFull: Structural Pattern Analysis
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