A stacking based deep learning framework integrating random search neural architecture search for meniscus tear diagnosis.

Seyyarer E., Genç H., Ayata F.

Journal of applied clinical medical physics, cilt.27, sa.8, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 8
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1002/acm2.70711
  • Dergi Adı: Journal of applied clinical medical physics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, EMBASE, MEDLINE, Directory of Open Access Journals, Health Research Premium Collection (ProQuest)
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

Background

Accurate and rapid diagnosis of meniscal tears is crucial for effective management of sports‐related injuries and degenerative knee disorders. Magnetic resonance imaging (MRI) is widely used for meniscus evaluation; however, manual interpretation is time‐consuming and subject to inter‐observer variability. Automated and reliable classification systems may therefore support clinical decision‐making.

Purpose

This study aims to develop a robust and interpretable deep learning framework for the automatic four‐class classification of meniscal conditions using MRI images.

Methods

A total of 2,000 knee MRI images obtained from a tertiary care university hospital were categorized into four classes: Grade I, Grade II, Grade III, and normal meniscus. A Random Search‐based Neural Architecture Search (RS‐NAS) strategy was used to generate task‐specific CNN architectures. The class probability outputs of the top‐performing NAS‐derived CNN models were combined using a late‐fusion stacking strategy, with ElasticNet employed as the meta‐learner. The proposed framework was evaluated using both a single‐run assessment and a repeated validation protocol consisting of 10 independent runs with 5‐fold cross‐validation. Performance was assessed using ACC, F1‐score, MCC, AUC, and PR‐AUC. Statistical comparisons and explainability analyses, including Grad‐CAM and ElasticNet feature importance, were also performed.

Results

In the single‐run evaluation, the ElasticNet‐based stacking model achieved an ACC of 0.9300, F1‐score of 0.9304, and AUC of 0.9913. Under the repeated 10‐run 5‐fold cross‐validation protocol, the proposed model obtained an ACC of 0.9083 ± 0.0019, F1‐score of 0.9084 ± 0.0018, MCC of 0.8783 ± 0.0024, AUC of 0.9883 ± 0.0008, and PR‐AUC of 0.9719 ± 0.0020. The proposed stacking framework outperformed the best NAS CNN, DenseNet121‐TL, averaging ensemble, and majority voting ensemble across the main evaluation metrics. Grad‐CAM visualizations and feature importance analysis further indicated that the model relied on clinically meaningful image regions and informative base‐model predictions.

Conclusions

The integration of RS‐NAS with ElasticNet‐based late‐fusion stacking provides a stable, high‐performing, and interpretable framework for four‐class meniscus MRI classification. Although the single‐run evaluation showed higher peak performance, the repeated cross‐validation results provide a more reliable estimate of model robustness and generalization. Future studies should validate the proposed framework using independent multi‐center datasets and different imaging protocols.