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No new human or animal data were collected, so IRB approval and informed consent were not required. Experiments adhered to ethical medical imaging guidelines. The framework is for research purposes; clinical use requires regulatory approval and real-world validation.","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Informed consent was obtained from participants of the original datasets. All authors have reviewed and approved the manuscript and affirm that the work is original, not previously published, and not under consideration elsewhere. The corresponding author assumes full responsibility for the submission.","order":3,"name":"Ethics","label":"Consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Informed consent was obtained from all individual participants included in the study. This statement is to certify that all authors have seen and approved the manuscript being submitted to the journal of Radiological Physics and Technology. We warrant that the article is the authors' original work. We warrant that the article has not received prior publication and is not under consideration for publication elsewhere. On behalf of all co-authors, the corresponding author shall bear full responsibility for the submission. This research has not been submitted for publication, nor has it been published in whole or in part elsewhere. All authors agree that the author list is correct in its content and order.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study includes no experiments involving human participants or animals.","order":5,"name":"Ethics","label":"Research involving human participants and\/or animals","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Radiology-specific augmentation preserving diagnostic fidelity, validated by >0.91 correlation in feature distributions. Grayscale-optimized ResNet-50 with spatial\u2013channel attention for enhanced detection of subtle and overlapping thoracic pathologies. Entropy-guided recursive feature elimination (RFE) reducing feature dimensionality by >85% with minimal accuracy loss. Hybrid fuzzy\u2013neural classifier with confidence-weighted defuzzification for robust handling of uncertainty and borderline cases. Confidence-weighted defuzzification ensuring stable and interpretable outputs in ambiguous scenarios. Multi-magnification evaluation (\u00d71, \u00d72, \u00d75, \u00d720) across four public datasets, demonstrating scale robustness and generalizability.","order":6,"name":"Ethics","label":"Novelties","group":{"name":"EthicsHeading","label":"Declarations"}}]}}